# llama

LLaMA is a collection of foundation language models ranging from 7B to 65B parameters, released to the research community.

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## Understanding W8A8 INT8 LLM quantization: Accuracy and performance results

DevFeed: [Understanding W8A8 INT8 LLM quantization: Accuracy and performance results](<https://devfeed.tech/articles/understanding-w8a8-int8-llm-quantization-accuracy-and-performance-results-17433.md>)

Original publisher: [Read original article](<https://developers.redhat.com/articles/2026/09/14/understanding-w8a8-int8-llm-quantization-accuracy-and-performance-results>)

Author: Sana Fayyaz

Published: 2026-09-14T13:01:43Z

Content type: article

Language: en

Sources: [Red Hat](<https://devfeed.tech/sources/red-hat.md>), [Red Hat Developer](<https://devfeed.tech/sources/red-hat-developer.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>)

Tags: [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [compression](<https://devfeed.tech/tags/compression.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [performance](<https://devfeed.tech/tags/performance.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [zero-shot](<https://devfeed.tech/tags/zero-shot.md>)

### AI overview

The article evaluates W8A8 INT8 quantization of a Llama 3.1 8B Instruct model. It describes reducing the model from 14.9 GB to 8.0 GB with SmoothQuant and GPTQ, then compares the base and compressed models on four benchmarks to assess accuracy and performance.

### Source excerpt

In Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy, we compressed a Llama 3.1 8B Instruct model from 14.9 GB to 8.0 GB using 8-bit integer (INT8) W8A8 quantization with SmoothQuant and Generative Pre-trained Transformer Quantization (GPTQ). The post Understanding W8A8 INT8 LLM quantization: Accuracy and performance results appeared first on Red Hat Developer.

## Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy

DevFeed: [Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy](<https://devfeed.tech/articles/understanding-w8a8-int8-llm-quantization-half-the-size-better-performance-same-accuracy-12347.md>)

Original publisher: [Read original article](<https://developers.redhat.com/articles/2026/09/07/understanding-w8a8-int8-llm-quantization>)

Author: Sana Fayyaz

Published: 2026-09-07T07:01:34Z

Content type: article

Language: en

Sources: [Red Hat Developer](<https://devfeed.tech/sources/red-hat-developer.md>)

Topics: [quantization](<https://devfeed.tech/topics/quantization.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Post-training optimization](<https://devfeed.tech/topics/post-training-optimization.md>), [llama](<https://devfeed.tech/topics/llama.md>), [LLM Techniques](<https://devfeed.tech/topics/llm-techniques.md>)

Tags: [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [compression](<https://devfeed.tech/tags/compression.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [memory](<https://devfeed.tech/tags/memory.md>), [performance](<https://devfeed.tech/tags/performance.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [quantization](<https://devfeed.tech/tags/quantization.md>)

### AI overview

This article explains INT8 W8A8 quantization for Llama 3.1 8B using SmoothQuant and GPTQ. It describes how quantization reduces memory requirements and can improve serving performance while preserving model accuracy, including a reported 46% reduction in model size and planned benchmark testing with vLLM and llm-compressor.

### Source excerpt

Large language models are expensive to serve. A model like Llama 3.1 8B in Bfloat16 (BF16) precision occupies roughly 15 GB of GPU memory. In BF16, each of the 8 billion parameters takes 2 bytes to store, which adds up to roughly 15 GB for the weights--and that's not all. The GPU needs memory for the key-value (KV) cache to store context for active requests, alongside intermediate tensor outputs (activations, as we call them) generated during inference. The post Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy appeared first on Red Hat Developer.

## KV Cache Engineering for LLM Serving

DevFeed: [KV Cache Engineering for LLM Serving](<https://devfeed.tech/articles/kv-cache-engineering-for-llm-serving-18237.md>)

Original publisher: [Read original article](<https://blog.dailydoseofds.com/p/kv-cache-engineering-for-llm-serving>)

Author: Avi Chawla

Published: 2026-09-06T20:43:32Z

Content type: tutorial

Language: en

Sources: [Daily Dose of Data Science](<https://devfeed.tech/sources/daily-dose-of-data-science.md>)

Topics: [Caching](<https://devfeed.tech/topics/caching.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [LLM Techniques](<https://devfeed.tech/topics/llm-techniques.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [llama](<https://devfeed.tech/topics/llama.md>), [quantization](<https://devfeed.tech/topics/quantization.md>)

Tags: [cache](<https://devfeed.tech/tags/cache.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llmops](<https://devfeed.tech/tags/llmops.md>), [quantization](<https://devfeed.tech/tags/quantization.md>)

### AI overview

This article explains how KV cache growth affects GPU memory and attention costs during LLM serving. It organizes twelve optimization techniques by whether they reduce heads, layers, retained tokens, representation width, precision, state size, allocation waste, or duplicate memory, while noting the trade-offs and limits of each approach.

### Source excerpt

12 techniques to manage KV cache in production.

## How to Build Your First Evaluation Set Before You Have Users

DevFeed: [How to Build Your First Evaluation Set Before You Have Users](<https://devfeed.tech/articles/how-to-build-frontier-lab-quality-evals-with-daniel-mckinnon-ex-pm-at-meta-google-34979.md>)

Original publisher: [Read original article](<https://www.news.aakashg.com/p/how-to-build-your-first-eval>)

Author: Aakash Gupta

Published: 2026-07-28T21:49:23Z

Content type: tutorial

Language: en

Sources: [Product Growth](<https://devfeed.tech/sources/product-growth.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Claude](<https://devfeed.tech/topics/claude.md>), [ChatGPT](<https://devfeed.tech/topics/chatgpt.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Meta](<https://devfeed.tech/topics/meta.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [chatgpt](<https://devfeed.tech/tags/chatgpt.md>), [claude](<https://devfeed.tech/tags/claude.md>), [evals](<https://devfeed.tech/tags/evals.md>), [how-to](<https://devfeed.tech/tags/how-to.md>)

### AI overview

A practical guide to building an AI evaluation set before a product has users. It explains how to create the set in Claude or ChatGPT, score it, interpret the results, and use evals to support development and career growth.

### Source excerpt

He wrote evals for Gemini, for Llama, and for Ray-Ban Meta. Today he builds one from a blank spreadsheet, live!

## Operational Costs and Performance Constraints of Self-Hosting Llama-3

DevFeed: [Operational Costs and Performance Constraints of Self-Hosting Llama-3](<https://devfeed.tech/articles/the-expensive-cosplay-of-local-models-true-3-am-operational-cost-of-hosting-llama-3-39574.md>)

Original publisher: [Read original article](<https://ankit-rana.com/logs/22-expensive-cosplay-local-llama-inference-tco/>)

Author: hello@ankit-rana.com

Published: 2026-03-21T00:00:00Z

Content type: opinion

Language: en

Sources: [Ankit Rana | Mechanical Sympathy](<https://devfeed.tech/sources/ankit-rana-mechanical-sympathy.md>)

Topics: [llama](<https://devfeed.tech/topics/llama.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Latency](<https://devfeed.tech/topics/latency.md>)

Tags: [cuda](<https://devfeed.tech/tags/cuda.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [kv-cache](<https://devfeed.tech/tags/kv-cache.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [operations](<https://devfeed.tech/tags/operations.md>), [vram](<https://devfeed.tech/tags/vram.md>)

### AI overview

This opinion article argues that the total cost of self-hosting Llama-3 includes VRAM for model weights and per-user KV caches, memory-bandwidth limits, batching and latency trade-offs, idle GPU time, and operational staffing. It uses a Llama-3 70B example to illustrate why API savings may be offset by infrastructure and support costs.

### Source excerpt

Llama-3 70B at 16-bit precision needs roughly 140 GB of VRAM just to sit idle, before the KV cache that every concurrent user requires. Inference is memory-bandwidth-bound rather than compute-bound: an A100's roughly 2000 GB/s against a 140 GB weight set caps single-stream generation near 14 tokens per second before batching. Add idle GPU time, batching-versus-latency trade-offs, and an ML infra engineer on pager duty, and that salary alone can erase the API savings.

## DigitalOcean's Inference Optimized Image improves Llama 3.3 70B inference efficiency on GPU Droplets

DevFeed: [DigitalOcean's Inference Optimized Image improves Llama 3.3 70B inference efficiency on GPU Droplets](<https://devfeed.tech/articles/digitalocean-gradienttm-ai-gpu-droplets-optimized-for-inference-increasing-throughput-at-lower-the-cost-19889.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/inference-optimized-image-droplet>)

Author: Hemasumanth Rasineni

Published: 2026-02-19T14:42:18Z

Content type: article

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Inference](<https://devfeed.tech/topics/inference.md>), [Digital Ocean](<https://devfeed.tech/topics/digital-ocean.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [llama](<https://devfeed.tech/topics/llama.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [quantization](<https://devfeed.tech/topics/quantization.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [cost](<https://devfeed.tech/tags/cost.md>), [digitalocean](<https://devfeed.tech/tags/digitalocean.md>), [droplets](<https://devfeed.tech/tags/droplets.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llama](<https://devfeed.tech/tags/llama.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [running](<https://devfeed.tech/tags/running.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

This article describes DigitalOcean's Inference Optimized Image, a pre-configured OS image for GPU Droplets that combines several inference optimizations. In the reported test, Llama 3.3 70B achieved higher throughput, lower time to first token, and lower cost per million tokens while running on two H100 GPUs instead of four.

### Source excerpt

Production-grade LLM inference demands more than just access to GPUs; it requires deep optimization across the entire serving stack, from quantization and attention kernels to memory management and parallelism strategies. Most teams deploying models like Llama 3.3 70B on vanilla configurations are leaving the majority of their hardware's capability on the table: underutilized FLOPs, wasted memory bandwidth, and GPU hours spent waiting instead of computing. To solve this, we built the Inference Optimized Image a fully pre-configured OS image available on DigitalOcean's GPU Droplets -- that layers speculative decoding, FP8 quantization, FlashAttention-3, paged attention, concurrent optimization, and prompt caching into a single deployable image. The result of our particular test: 143% higher throughput (2,000 vs. 823 tokens/second), 40.7% lower TTFT (187.9ms vs. 316.83ms), and a 75% reduction in cost per million tokens ($1.472 vs. $5.80) -- all while running Llama 3.3 70B on 2 H100 GPUs instead of 4. In this post, we walk through the optimization stack, the engineering reasoning behind each layer, and the benchmark methodology and our test results showing these gains. Prefill, Decode, and Why Optimization is Multiplicative As we covered in our LLM Inference Benchmarking post, inference works in two distinct phases with fundamentally different computation characteristics. The prefill phase processes the entire input prompt through the model's forward pass self-attention, layer norms, feed-forward networks and is compute-bound, with high arithmetic intensity (FLOPs per byte transferred). The decode phase generates tokens one at a time, loading the full weight matrix and KV cache from HBM for each token, making it strictly memory-bandwidth-bound. This distinction matters because each optimization in our stack targets a specific bottleneck. Speculative decoding attacks the sequential nature of decode. FP8 quantization reduces memory footprint and accelerates compute via hig

## Measuring Open-Source Llama Nemotron Models on DeepResearch Bench

DevFeed: [Measuring Open-Source Llama Nemotron Models on DeepResearch Bench](<https://devfeed.tech/articles/measuring-open-source-llama-nemotron-models-on-deepresearch-bench-7376.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/nvidia/ai-q-top-ranking-open-portable-deep-research-agent>)

Author: Jay Rodge

Published: 2025-08-04T19:51:50Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [ai](<https://devfeed.tech/tags/ai.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hallucination-detection](<https://devfeed.tech/tags/hallucination-detection.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>)

### AI overview

The article presents NVIDIA's AI-Q Blueprint as a portable, open deep research agent that reached the top of Hugging Face's "LLM with Search" leaderboard on DeepResearch Bench. It combines Llama 3.3-70B Instruct with Llama-3.3-Nemotron-Super-49B-v1.5 for retrieval, reasoning, synthesis, query planning, and tool use, and incorporates NVIDIA NeMo Retriever and the NVIDIA NeMo Agent toolkit. The architecture emphasizes long-context, multimodal, low-latency search over local and web data, privacy, compliance, on-premise deployment, transparent model lineage, and efficient inference on GPUs.

### Source excerpt

NVIDIA's AI-Q Blueprint--the leading portable, open deep research agent--recently climbed to the top of the Hugging Face "LLM with Search" leaderboard on DeepResearch Bench. This is a significant step forward for the open-source AI stack, proving that developer-accessible models can power advanced agentic workflows that rival or surpass closed alternatives. What sets AI-Q apart?

## Creating custom kernels for the AMD MI300

DevFeed: [Creating custom kernels for the AMD MI300](<https://devfeed.tech/articles/creating-custom-kernels-for-the-amd-mi300-7347.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/mi300kernels>)

Author: Rémi Ouazan Reboul; seungrok jung

Published: 2025-07-09T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [kernels](<https://devfeed.tech/topics/kernels.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [amd](<https://devfeed.tech/tags/amd.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [kernels](<https://devfeed.tech/tags/kernels.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llama](<https://devfeed.tech/tags/llama.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

Hugging Face and AMD describe open-source, device-specific kernel optimizations for serving Llama 3.1 405B in FP8 with vLLM on eight AMD MI300X GPUs. The article explains kernel-level optimization and reports latency improvements from fused residual, normalization, FP8 conversion, SwiGLU, and Skinny GEMM kernels.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Welcome the NVIDIA Llama Nemotron Nano VLM to Hugging Face Hub

DevFeed: [Welcome the NVIDIA Llama Nemotron Nano VLM to Hugging Face Hub](<https://devfeed.tech/articles/welcome-the-nvidia-llama-nemotron-nano-vlm-to-hugging-face-hub-7384.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/nvidia/llama-nemotron-nano-vl>)

Author: Amanda Saunders; Amala Sanjay Deshmukh; Kateryna Chumachenko; Annie Surla; Karan; Tuomas Rintamaki; Matthieu Le; Yu Yao; Chen Cui; Timo Roman; Zhiding Yu; Mike Ranzinger

Published: 2025-06-27T21:09:27Z

Content type: release

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [NeMo](<https://devfeed.tech/topics/nemo.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [idp](<https://devfeed.tech/tags/idp.md>), [llama](<https://devfeed.tech/tags/llama.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [nemo](<https://devfeed.tech/tags/nemo.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [ocr](<https://devfeed.tech/tags/ocr.md>), [performance](<https://devfeed.tech/tags/performance.md>), [recognition](<https://devfeed.tech/tags/recognition.md>), [train](<https://devfeed.tech/tags/train.md>), [use-cases](<https://devfeed.tech/tags/use-cases.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

NVIDIA Llama Nemotron Nano VL is an 8B vision-language model for intelligent document processing. Available on Hugging Face, it extracts and interprets text, tables, charts, diagrams, and other information from complex documents.

### Source excerpt

NVIDIA Llama Nemotron Nano VL is a state-of-the-art 8B Vision Language Model (VLM) designed for intelligent document processing, offering high accuracy and multimodal understanding. Available on Hugging Face, it excels in extracting and understanding information from complex documents like invoices, receipts, contracts, and more.

## nanoVLM: The simplest repository to train your VLM in pure PyTorch

DevFeed: [nanoVLM: The simplest repository to train your VLM in pure PyTorch](<https://devfeed.tech/articles/nanovlm-the-simplest-repository-to-train-your-vlm-in-pure-pytorch-7363.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/nanovlm>)

Author: Aritra Roy Gosthipaty; Luis; Andres Marafioti; Sergio Paniego; merve; Pedro Cuenca; Vaibhav Srivastav

Published: 2025-05-21T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [community](<https://devfeed.tech/tags/community.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [nanovlm](<https://devfeed.tech/tags/nanovlm.md>), [object-detection](<https://devfeed.tech/tags/object-detection.md>), [open](<https://devfeed.tech/tags/open.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [research](<https://devfeed.tech/tags/research.md>), [train](<https://devfeed.tech/tags/train.md>), [training](<https://devfeed.tech/tags/training.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

nanoVLM is a minimal, readable pure-PyTorch toolkit for building and training vision-language models. It processes images and text to generate text, focuses on visual question answering, and is designed to help beginners explore VLMs through a compact codebase and guided training workflow.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Welcoming Llama Guard 4 on Hugging Face Hub

DevFeed: [Welcoming Llama Guard 4 on Hugging Face Hub](<https://devfeed.tech/articles/welcoming-llama-guard-4-on-hugging-face-hub-7333.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/llama-guard-4>)

Author: merve; Aritra Roy Gosthipaty; Sergio Paniego; Pedro Cuenca

Published: 2025-04-29T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [llama](<https://devfeed.tech/topics/llama.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [large-language-models](<https://devfeed.tech/topics/large-language-models.md>), [moe](<https://devfeed.tech/topics/moe.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [safety](<https://devfeed.tech/tags/safety.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

Hugging Face introduces Llama Guard 4, a multimodal safety model for detecting unsafe image and text inputs and outputs. The dense 12B model can run on a single 24 GB GPU, supports multilingual content, and classifies hazards for configurable moderation pipelines.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Welcome Llama 4 Maverick & Scout on Hugging Face

DevFeed: [Welcome Llama 4 Maverick & Scout on Hugging Face](<https://devfeed.tech/articles/welcome-llama-4-maverick-scout-on-hugging-face-7338.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/llama4-release>)

Author: ben burtenshaw; Vaibhav Srivastav; Pedro Cuenca; Clem 🤗; Rajat Arya; Jared Sulzdorf; Lysandre

Published: 2025-04-05T00:00:00Z

Content type: release

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [llama](<https://devfeed.tech/topics/llama.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Transformers](<https://devfeed.tech/topics/transformers.md>), [tgi](<https://devfeed.tech/topics/tgi.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>)

Tags: [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llama](<https://devfeed.tech/tags/llama.md>), [meta](<https://devfeed.tech/tags/meta.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [tgi](<https://devfeed.tech/tags/tgi.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

Hugging Face announces the release of Meta's Llama 4 Maverick and Scout, natively multimodal models built with an autoregressive Mixture-of-Experts architecture. The article describes their parameter sizes, expert counts, training data, language support, quantization options, licensing, and integration with the Hugging Face Hub, Transformers, TRL, and TGI.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## AI-буткемп от red\_mad\_robot: прокачиваем скиллы в NLP и MarkUp

DevFeed: [AI-буткемп от red\_mad\_robot: прокачиваем скиллы в NLP и MarkUp](<https://devfeed.tech/articles/ai-red-mad-robot-nlp-markup-24009.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/redmadrobot/articles/884698/>)

Author: redmadrobot (red\_mad\_robot)

Published: 2025-02-21T12:37:26Z

Content type: release

Language: ru

Sources: [Redmadrobot EN](<https://devfeed.tech/sources/redmadrobot-en.md>), [Redmadrobot RU](<https://devfeed.tech/sources/redmadrobot-ru.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [genai](<https://devfeed.tech/topics/genai.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [ChatGPT](<https://devfeed.tech/topics/chatgpt.md>), [Claude](<https://devfeed.tech/topics/claude.md>), [llama](<https://devfeed.tech/topics/llama.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [chatgpt](<https://devfeed.tech/tags/chatgpt.md>), [claude](<https://devfeed.tech/tags/claude.md>), [genai](<https://devfeed.tech/tags/genai.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [markup](<https://devfeed.tech/tags/markup.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [rag](<https://devfeed.tech/tags/rag.md>), [tag-77ff283d3734](<https://devfeed.tech/tags/tag-77ff283d3734.md>), [tag-86b843454893](<https://devfeed.tech/tags/tag-86b843454893.md>), [tag-a6eb30d5e1b3](<https://devfeed.tech/tags/tag-a6eb30d5e1b3.md>), [tag-e076f05a03bb](<https://devfeed.tech/tags/tag-e076f05a03bb.md>)

### AI overview

red_mad_robot announces a free four-week AI bootcamp for junior developers, with tracks for NLP engineers and MarkUp engineers. The program includes practical work, senior mentorship, collaborative AI development, and a portfolio case. Applications are accepted until March 10, and the bootcamp starts March 17.

### Source excerpt

Хотите ворваться в разработку AI-решений? red_mad_robot запускает интенсивный буткемп для джунов, где за 4 недели вы освоите востребованные навыки и получите практический опыт в разработке AI-продуктов под руководством ведущих экспертов индустрии Читать далее

## Open R1: Update #2

DevFeed: [Open R1: Update #2](<https://devfeed.tech/articles/open-r1-update-2-7422.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/open-r1/update-2>)

Author: Loubna Ben Allal; Lewis Tunstall; Anton Lozhkov; Elie Bakouch; Guilherme Penedo; Hynek Kydlicek; Gabriel Martín Blázquez

Published: 2025-02-10T16:10:47Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [deepseek](<https://devfeed.tech/topics/deepseek.md>), [synthetic-data](<https://devfeed.tech/topics/synthetic-data.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Chain-of-thought](<https://devfeed.tech/topics/chain-of-thought.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [math](<https://devfeed.tech/topics/math.md>), [llama](<https://devfeed.tech/topics/llama.md>), [qwen](<https://devfeed.tech/topics/qwen.md>), [sglang](<https://devfeed.tech/topics/sglang.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [math-verify](<https://devfeed.tech/topics/math-verify.md>), [Parser](<https://devfeed.tech/topics/parser.md>)

Tags: [chain-of-thought](<https://devfeed.tech/tags/chain-of-thought.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [llama](<https://devfeed.tech/tags/llama.md>), [math](<https://devfeed.tech/tags/math.md>), [math-verify](<https://devfeed.tech/tags/math-verify.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [sglang](<https://devfeed.tech/tags/sglang.md>), [synthetic-data](<https://devfeed.tech/tags/synthetic-data.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

Open R1 Update #2 presents OpenR1-Math-220k, a large-scale mathematical reasoning dataset created to help reconstruct parts of the DeepSeek R1 training pipeline and synthetic-data process. The article describes reasoning-trace generation, local inference with vLLM and SGLang, automated filtering with Math Verify, and the use of Llama3.3-70B-Instruct as a judge. It also discusses distillation and fine-tuning of Qwen and Llama models using reasoning traces.

### Source excerpt

We are now two weeks into the Open R1 project which aims to reconstruct the missing pieces of DeepSeek R1--specifically, the training pipeline and synthetic data. In this post, we are happy to share the construction of OpenR1-Math-220k: our first large-scale dataset for mathematical reasoning!

## Investing in Performance: Fine-tune small models with LLM insights - a CFM case study

DevFeed: [Investing in Performance: Fine-tune small models with LLM insights - a CFM case study](<https://devfeed.tech/articles/investing-in-performance-fine-tune-small-models-with-llm-insights-a-cfm-case-study-7139.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/cfm-case-study>)

Author: Oussama Ahouzi; champonnois; Jérémy L'Hour; Pirashanth Ratnamogan; Bérengère Patault; Morgane Goibert

Published: 2024-12-03T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [inference-endpoints](<https://devfeed.tech/topics/inference-endpoints.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [argilla](<https://devfeed.tech/topics/argilla.md>), [llama](<https://devfeed.tech/topics/llama.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [Finance](<https://devfeed.tech/topics/finance.md>), [Meta](<https://devfeed.tech/topics/meta.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Scalability](<https://devfeed.tech/topics/scalability.md>)

Tags: [argilla](<https://devfeed.tech/tags/argilla.md>), [case-studies](<https://devfeed.tech/tags/case-studies.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [expert-support](<https://devfeed.tech/tags/expert-support.md>), [expert-support-program](<https://devfeed.tech/tags/expert-support-program.md>), [financial-applications](<https://devfeed.tech/tags/financial-applications.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [inference-endpoints](<https://devfeed.tech/tags/inference-endpoints.md>), [llm](<https://devfeed.tech/tags/llm.md>), [models](<https://devfeed.tech/tags/models.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [scalability](<https://devfeed.tech/tags/scalability.md>)

### AI overview

A case study of Capital Fund Management's use of open-source LLMs to improve financial named entity recognition. It covers LLM-assisted labeling, fine-tuning smaller models with curated datasets, and deployment on Hugging Face Inference Endpoints to balance accuracy, cost, and scalability.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Llama can now see and run on your device - welcome Llama 3.2

DevFeed: [Llama can now see and run on your device - welcome Llama 3.2](<https://devfeed.tech/articles/llama-can-now-see-and-run-on-your-device-welcome-llama-3-2-7337.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/llama32>)

Author: merve; Philipp Schmid; Omar Sanseviero; Vaibhav Srivastav; Lewis Tunstall; Aritra Roy Gosthipaty; Pedro Cuenca

Published: 2024-09-25T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [llama](<https://devfeed.tech/topics/llama.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Meta](<https://devfeed.tech/topics/meta.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [inference-endpoints](<https://devfeed.tech/topics/inference-endpoints.md>), [tgi](<https://devfeed.tech/topics/tgi.md>), [Transformers](<https://devfeed.tech/topics/transformers.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-endpoints](<https://devfeed.tech/tags/inference-endpoints.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [meta](<https://devfeed.tech/tags/meta.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [on-device](<https://devfeed.tech/tags/on-device.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [tgi](<https://devfeed.tech/tags/tgi.md>), [transformers](<https://devfeed.tech/tags/transformers.md>), [vision](<https://devfeed.tech/tags/vision.md>)

### AI overview

Meta's Llama 3.2 release introduces multimodal Vision models in 11B and 90B sizes, smaller text-only 1B and 3B models for on-device use, and vision-enabled Llama Guard 3. The article describes their capabilities, architecture, supported languages, inference examples, and integrations with Hugging Face Transformers, TGI, Inference Endpoints, Google Cloud, Amazon SageMaker, and DELL Enterprise Hub.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Deploy Meta Llama 3.1 405B on Google Cloud Vertex AI

DevFeed: [Deploy Meta Llama 3.1 405B on Google Cloud Vertex AI](<https://devfeed.tech/articles/deploy-meta-llama-3-1-405b-on-google-cloud-vertex-ai-7336.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/llama31-on-vertex-ai>)

Author: Alvaro Bartolome; Philipp Schmid; Simon Pagezy; Jeff Boudier

Published: 2024-08-19T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [llama](<https://devfeed.tech/topics/llama.md>), [Google Cloud Platform (GCP)](<https://devfeed.tech/topics/google-cloud.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [tgi](<https://devfeed.tech/topics/tgi.md>), [Meta](<https://devfeed.tech/topics/meta.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Containers](<https://devfeed.tech/topics/containers.md>)

Tags: [container](<https://devfeed.tech/tags/container.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [gcp](<https://devfeed.tech/tags/gcp.md>), [google-cloud](<https://devfeed.tech/tags/google-cloud.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llama](<https://devfeed.tech/tags/llama.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [vertex](<https://devfeed.tech/tags/vertex.md>), [vertex-ai](<https://devfeed.tech/tags/vertex-ai.md>)

### AI overview

This tutorial explains how to programmatically deploy the FP8-quantized Meta Llama 3.1 405B model on Google Cloud Vertex AI using Text Generation Inference and Hugging Face Deep Learning Containers. It covers deployment on an A3 node with eight NVIDIA H100 GPUs, alternative deployment paths, and the memory considerations for running the model.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## XetHub is joining Hugging Face!

DevFeed: [XetHub is joining Hugging Face!](<https://devfeed.tech/articles/xethub-is-joining-hugging-face-7571.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/xethub-joins-hf>)

Author: yuchenglow; Julien Chaumond

Published: 2024-08-08T00:00:00Z

Content type: news

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [xet](<https://devfeed.tech/topics/xet.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Git](<https://devfeed.tech/topics/git.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [parquet](<https://devfeed.tech/topics/parquet.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [ai-development](<https://devfeed.tech/tags/ai-development.md>), [announcement](<https://devfeed.tech/tags/announcement.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [git](<https://devfeed.tech/tags/git.md>), [hub](<https://devfeed.tech/tags/hub.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [llama](<https://devfeed.tech/tags/llama.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [parquet](<https://devfeed.tech/tags/parquet.md>), [xet](<https://devfeed.tech/tags/xet.md>)

### AI overview

Hugging Face announces that XetHub is joining the organization to improve storage and versioning for large AI datasets and models. XetHub's technology uses chunking and deduplication so updates can upload only changed portions instead of entire large files, while helping Git scale to very large repositories.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Building a RAG application with Llama 3.1 and pgvector

DevFeed: [Building a RAG application with Llama 3.1 and pgvector](<https://devfeed.tech/articles/building-a-rag-application-with-llama-3-1-and-pgvector-5093.md>)

Original publisher: [Read original article](<https://neon.com/blog/building-a-rag-application-with-llama-3-1-and-pgvector>)

Author: Andrew Tate

Published: 2024-07-30T16:58:41Z

Content type: tutorial

Language: en

Sources: [Blog -- Neon Docs](<https://devfeed.tech/sources/blog-neon-docs.md>)

Topics: [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>), [Meta](<https://devfeed.tech/topics/meta.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [database](<https://devfeed.tech/tags/database.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [llama](<https://devfeed.tech/tags/llama.md>), [meta](<https://devfeed.tech/tags/meta.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [openai](<https://devfeed.tech/tags/openai.md>), [pgvector](<https://devfeed.tech/tags/pgvector.md>), [product](<https://devfeed.tech/tags/product.md>), [rag](<https://devfeed.tech/tags/rag.md>)

### AI overview

A tutorial on building a Retrieval-Augmented Generation application with Meta's Llama 3.1 and pgvector. It explains how retrieval and embeddings let an LLM use relevant, up-to-date information from an external knowledge base.

### Source excerpt

The AI wars have begun. Not the one where the machines enslave us and use our body heat to power their compute-that's at least 18 months off. 🙂 No, this AI war is between the tech giants and between closed, proprietary models and open source. In one corner, we have OpenAI, suppo...

## AI Power Plays: Partnerships and Rivals 🤝🚀 - Air Around AI (a3) #9

DevFeed: [AI Power Plays: Partnerships and Rivals 🤝🚀 - Air Around AI (a3) #9](<https://devfeed.tech/articles/ai-power-plays-partnerships-and-rivals-air-around-ai-a3-9-38785.md>)

Original publisher: [Read original article](<https://airaroundai.substack.com/p/ai-power-plays-partnerships-and-rivals>)

Author: Pradeep Kumar

Published: 2024-07-29T14:30:58Z

Content type: article

Language: en

Sources: [Air Around AI](<https://devfeed.tech/sources/air-around-ai.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>), [Google](<https://devfeed.tech/topics/google.md>), [Google Search](<https://devfeed.tech/topics/google-search.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Meta](<https://devfeed.tech/topics/meta.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [text-to-image](<https://devfeed.tech/topics/text-to-image.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [aws](<https://devfeed.tech/tags/aws.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [google](<https://devfeed.tech/tags/google.md>), [google-search](<https://devfeed.tech/tags/google-search.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llms](<https://devfeed.tech/tags/llms.md>), [meta](<https://devfeed.tech/tags/meta.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [openai](<https://devfeed.tech/tags/openai.md>)

### AI overview

This article surveys recent AI industry developments, including the Microsoft-OpenAI partnership, SearchGPT, Meta's Llama 3.1, Mistral Large 2, Adobe Firefly 3, and Google's Gemini 1.5 Flash update. It also discusses competition among major companies and the computing demands of larger models.

### Source excerpt

Meta got Llama 3.1, Mistral brings Large 2, OpenAI flexes SearchGPT, Adobe gifts Firefly, Google offers faster Gemini, xAI promises largest supercomputer, CNN Explorer

## Serverless Inference with Hugging Face and NVIDIA NIM

DevFeed: [Serverless Inference with Hugging Face and NVIDIA NIM](<https://devfeed.tech/articles/serverless-inference-with-hugging-face-and-nvidia-nim-7276.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/inference-dgx-cloud>)

Author: Philipp Schmid; Jeff Boudier

Published: 2024-07-29T00:00:00Z

Content type: release

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [NVIDIA NIM](<https://devfeed.tech/topics/nvidia-nim.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-serving](<https://devfeed.tech/topics/model-serving.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [DGX Cloud](<https://devfeed.tech/topics/dgx-cloud.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [API](<https://devfeed.tech/topics/api.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Access Control](<https://devfeed.tech/topics/access-control.md>), [Claude](<https://devfeed.tech/topics/claude.md>)

Tags: [access-control](<https://devfeed.tech/tags/access-control.md>), [api](<https://devfeed.tech/tags/api.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [dgx-cloud](<https://devfeed.tech/tags/dgx-cloud.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-dgx](<https://devfeed.tech/tags/nvidia-dgx.md>), [nvidia-nim](<https://devfeed.tech/tags/nvidia-nim.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [pricing](<https://devfeed.tech/tags/pricing.md>), [serverless](<https://devfeed.tech/tags/serverless.md>)

### AI overview

Hugging Face announces a serverless NVIDIA NIM API on the Hugging Face Hub for Enterprise Hub organizations. The service provides API access to open generative AI models, including Llama and Mistral, on NVIDIA DGX Cloud infrastructure, with pay-as-you-go pricing and a guide for creating fine-grained organization tokens. The article notes that the service was deprecated and unavailable as of April 10, 2025.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Llama 3.1 - 405B, 70B & 8B with multilinguality and long context

DevFeed: [Llama 3.1 - 405B, 70B & 8B with multilinguality and long context](<https://devfeed.tech/articles/llama-3-1-405b-70b-8b-with-multilinguality-and-long-context-7335.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/llama31>)

Author: Philipp Schmid; Omar Sanseviero; Alvaro Bartolome; Leandro von Werra; Daniel Vila; Vaibhav Srivastav; Marc Sun; Pedro Cuenca

Published: 2024-07-23T00:00:00Z

Content type: release

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [llama](<https://devfeed.tech/topics/llama.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Meta](<https://devfeed.tech/topics/meta.md>), [Inference](<https://devfeed.tech/topics/inference.md>)

Tags: [community](<https://devfeed.tech/tags/community.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [meta](<https://devfeed.tech/tags/meta.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [research](<https://devfeed.tech/tags/research.md>)

### AI overview

Hugging Face describes the Llama 3.1 release: six base and instruction-tuned models in 8B, 70B, and 405B sizes, plus Llama Guard 3 and Prompt Guard. The models support 128K-token context lengths and eight languages, and the article covers integrations, quantization, fine-tuning, and synthetic-data workflows.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Why Meta's Llama 3.1 405B Matters for Open-Source AI

DevFeed: [Why Meta's Llama 3.1 405B Matters for Open-Source AI](<https://devfeed.tech/articles/why-the-llama-3-1-announcement-is-huge-33432.md>)

Original publisher: [Read original article](<https://timkellogg.me/blog/2024/07/23/llama-3.1>)

Published: 2024-07-23T00:00:00Z

Content type: opinion

Language: en

Sources: [Tim Kellogg](<https://devfeed.tech/sources/tim-kellogg.md>)

Topics: [llama](<https://devfeed.tech/topics/llama.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>), [Meta](<https://devfeed.tech/topics/meta.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [llama](<https://devfeed.tech/tags/llama.md>), [meta](<https://devfeed.tech/tags/meta.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [open-source-ai](<https://devfeed.tech/tags/open-source-ai.md>), [qualcomm](<https://devfeed.tech/tags/qualcomm.md>)

### AI overview

This opinion article argues that Meta's Llama 3.1 405B is significant because it brings a frontier-quality open-source model to developers and companies. It highlights potential benefits including data sovereignty, lower costs, and greater independence from proprietary AI services.

### Source excerpt

Today Meta announced Llama 3.1 405B as an open source AI model. Here's why this is a game-chager for the AI landscape.

## Announcing New Hugging Face and KerasHub integration

DevFeed: [Announcing New Hugging Face and KerasHub integration](<https://devfeed.tech/articles/announcing-new-hugging-face-and-kerashub-integration-7301.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/keras-hub-integration>)

Author: Aritra Roy Gosthipaty

Published: 2024-07-10T00:00:00Z

Content type: news

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Keras](<https://devfeed.tech/topics/keras.md>), [Transformers](<https://devfeed.tech/topics/transformers.md>), [gemma](<https://devfeed.tech/topics/gemma.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [gemma](<https://devfeed.tech/tags/gemma.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [json](<https://devfeed.tech/tags/json.md>), [keras](<https://devfeed.tech/tags/keras.md>), [llama](<https://devfeed.tech/tags/llama.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [open-source-collab](<https://devfeed.tech/tags/open-source-collab.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [tensorflow](<https://devfeed.tech/tags/tensorflow.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

The article announces an integration between Hugging Face Transformers and KerasHub through a shared model save format. It allows KerasHub users to load many Transformers checkpoints, initially including Gemma, Llama 3, and PaliGemma, and use them with TensorFlow, JAX, or PyTorch backends. The integration handles conversion of configuration variables, weight names, and tokenizer vocabularies internally.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

[Next page](<https://devfeed.tech/topics/llama.md?cursor=WyIyMDI0LTA3LTEwVDAwOjAwOjAwKzAwOjAwIiwgIjkxMzgwYzZiLTRlYWEtNDQyZi1hODFlLWZiNGZjOGU5MmVhMSJd>)