# llama Published articles for llama. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The generative AI customization spectrum: From prompt engineering to custom models on AWS DevFeed: [The generative AI customization spectrum: From prompt engineering to custom models on AWS]() Original publisher: [Read original article]() Author: Bhavya Sruthi Sode Published: 2026-09-14T15:47:12Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Generative AI](), [Amazon Web Services](), [Prompt Engineering](), [Fine-tuning](), [Retrieval Augmented Generation (RAG)](), [Amazon Bedrock](), [foundation-models](), [Anthropic Claude](), [Nova](), [llama]() Tags: [advanced-300](), [ai](), [amazon-bedrock](), [anthropic-claude](), [aws](), [best-practices](), [chain-of-thought](), [fine-tuning](), [foundation-models](), [generative-ai](), [llama](), [nova](), [optimization](), [prompt-engineering](), [rag]() ### AI overview This AWS article presents an eight-step decision framework for customizing generative AI workloads. It compares progressively more involved approaches, including prompt engineering, Retrieval Augmented Generation (RAG), fine-tuning, continued pre-training, and custom models such as Amazon Nova Forge, emphasizing that teams should start with the simplest approach and escalate when greater control or domain specificity is required. ### Source excerpt Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simple and escalate only when you must. ## Understanding W8A8 INT8 LLM quantization: Accuracy and performance results DevFeed: [Understanding W8A8 INT8 LLM quantization: Accuracy and performance results]() Original publisher: [Read original article]() Author: Sana Fayyaz Published: 2026-09-14T13:01:43Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Large Language Model](), [quantization](), [llama](), [Compression](), [Benchmark](), [dataset](), [Algorithms]() Tags: [ai-inference](), [artificial-intelligence](), [benchmark](), [benchmarks](), [compression](), [evaluation](), [hardware](), [llama](), [llm](), [model](), [models](), [performance](), [quantization](), [reasoning](), [zero-shot]() ### 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. ## On-Device AI Series (Part 5): LiteRT-LM DevFeed: [On-Device AI Series (Part 5): LiteRT-LM]() Original publisher: [Read original article]() Author: Oğuzhan Aslan Published: 2026-09-14T05:59:12Z Content type: tutorial Language: en Sources: [ProAndroidDev - Medium]() Topics: [LiteRT](), [On-device AI](), [Large Language Model](), [Inference](), [Streaming](), [GPU](), [LLM evaluation / benchmarking](), [PyTorch]() Tags: [ai](), [android](), [android-development](), [benchmarking](), [gpu](), [inference](), [litert](), [llama](), [llm](), [multimodal](), [on-device](), [on-device-ai](), [programming](), [pytorch](), [qwen](), [software-development](), [streaming]() ### AI overview This tutorial explains LiteRT-LM for running large language models on-device. It covers the Engine/Session API, streaming output, system prompts, tool calling, multimodal inputs, thinking mode, and CPU-versus-GPU benchmarking. The article also discusses tradeoffs involving privacy, network independence, latency, memory, sampling configuration, and model capability compared with cloud APIs. ### Source excerpt Put your phone in airplane mode. Open the app, type a question, and watch the answer arrive one token at a time -- no spinner waiting on a network round-trip, no API key, no per-token bill, and nothing you typed ever leaving the device. LiteRT-LM removes the genuinely hard parts of running an LLM on-device -- KV-cache management, token streaming, backend selection -- but it doesn't remove your job so much as relocate it. What's left on your plate is a short, specific list: sizing a combined input+output token budget, owning your own sampling defaults, hand-building system prompts and tool calling out of raw text, and one native-library collision that presents as a SIGSEGV rather than a build error. Know those going in and the API itself is a clean three-step pattern. We'll get there in that order: Why you'd choose this runtime and what it costs you versus the cloud. The Engine/Session model you need to read the code at all. Real implementation samples -- streaming, system prompts and tool calling, multimodal inputs, thinking mode, and CPU-vs-GPU benchmarking. The anti-patterns to avoid. A developer-friendliness rating on the same rubric as Parts 1-4. Why Use LiteRT-LM? You reach for LiteRT-LM instead of hand-rolling generation on top of raw LiteRT when: You need multi-turn conversation, not single-shot inference -- session state and KV-cache bookkeeping are handled for you, and resetting a conversation is a session swap, not a model reload. You need streaming output -- token-by-token delivery for a responsive chat UI, instead of a blocking call that returns everything at once. You're choosing between CPU and GPU per device -- the explicit backend parameter turns that into a runtime decision instead of a build-time guess. You want a pre-converted model without doing your own PyTorch-to-LiteRT conversion work -- the Model Zoo covers Gemma, Qwen, Llama, and more out of the box. You're willing to own sampling -- the engine won't pick sane decoding defaults for you; that's on the ## Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference DevFeed: [Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference]() Original publisher: [Read original article]() Author: Kareem Syed-Mohammed Published: 2026-09-10T21:58:09Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [Large Language Model](), [Low-Latency Inference](), [Caching](), [Inference](), [Inference Performance]() Tags: [advanced-300](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [announcements](), [benchmarks](), [caching](), [inference](), [latency](), [llama](), [llm](), [routing](), [tensorrt-llm](), [vllm]() ### AI overview Amazon SageMaker Inference introduces prefix-aware routing for LLM requests. By consistently sending requests with matching prompt prefixes to the same instance, it improves reuse of cached KV computations and can reduce time to first token. ### Source excerpt Amazon SageMaker Inference now offers prefix-aware routing, a routing strategy that sends requests sharing the same prompt prefix to the same instance so the KV cache stays warm. In benchmarks on Llama 3.1 70B, it reduced P50 time-to-first-token by up to 77% and raised KV cache hit rates from about 25% to over 80%. ## 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]() Original publisher: [Read original article]() Author: Sana Fayyaz Published: 2026-09-07T07:01:34Z Content type: article Language: en Sources: [Red Hat Developer]() Topics: [quantization](), [Large Language Model](), [GPU](), [Inference](), [model-deployment](), [Post-training optimization](), [llama](), [LLM Techniques]() Tags: [ai-inference](), [artificial-intelligence](), [compression](), [deployment](), [gpu](), [inference](), [language-models](), [large-language-models](), [llama](), [llm](), [memory](), [performance](), [post-training](), [quantization]() ### 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]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-06T20:43:32Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Caching](), [Large Language Model](), [LLM Techniques](), [GPU](), [Inference](), [llama](), [quantization]() Tags: [cache](), [engineering](), [gpu](), [inference](), [llama](), [llm](), [llmops](), [quantization]() ### 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. ## Up to 3.2x Faster Inference with LFM2.5-DSpark DevFeed: [Up to 3.2x Faster Inference with LFM2.5-DSpark]() Original publisher: [Read original article]() Author: Xx; Leonie Monigatti; Fernando Fernandes Neto; Tarek Dakhran; Nathan Ranchin Published: 2026-08-20T16:52:57Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Inference](), [Large Language Model](), [Latency](), [Streaming](), [sglang](), [GPU](), [Code]() Tags: [agentic](), [benchmark](), [code](), [gpu](), [inference](), [latency](), [llama](), [llama-cpp](), [llm](), [memory](), [on-device](), [streaming]() ### AI overview The article presents LFM2.5-DSpark, a speculative decoding approach that accelerates LLM inference by using a lightweight draft model and verifier. It reports up to 3.18x GPU throughput improvement and up to 2.87x on-device improvement, along with reduced function-calling latency and support for llama.cpp and SGLang. ### Source excerpt - Faster inference: up to 3.18 throughput improvement on a GPU and up to 2.87x on-device. - Toward on-device agentic inference: cuts function-calling latency by 57% on average for LFM2.5-2.6B - Day-one support for llama.cpp and SGLang: LFM-compatible DSpark integration is open-sourced upstream The decode phase in LLM inference is traditionally memory-bound. Most latency comes from streaming weights from DRAM into SRAM, not from intense computation. ## Running Ollama Locally with Podman on Fedora Linux DevFeed: [Running Ollama Locally with Podman on Fedora Linux]() Original publisher: [Read original article]() Author: Yazan Monshed Published: 2026-08-05T08:00:00Z Content type: article Language: en Sources: [Fedora Magazine]() Topics: [Fedora](), [Large Language Model](), [Linux](), [Open Source Models & Datasets](), [API](), [cURL](), [GPU]() Tags: [ai-development](), [api](), [curl](), [fedora-project-community](), [gpu](), [large-language-models-llms](), [linux](), [llama](), [nvidia](), [offline](), [ollama](), [open-source](), [podman](), [privacy](), [python](), [terminal](), [using-software]() ### AI overview This article explains how to run Ollama locally in a Podman container on Fedora Linux. It covers installing or verifying Podman, creating persistent storage for model weights, running the Ollama container, downloading Llama 3, optionally enabling Nvidia GPU acceleration, and using Ollama's local REST API with curl. ### Source excerpt Running Large Language Models (LLMs) locally has become increasingly popular for development, privacy, and offline testing. Ollama makes this incredibly straightforward, allowing you to run models like Llama 3 or Mistral directly on your machine. By leveraging Podman on Fedora Linux, you can isolate Ollama inside a container. This approach keeps your host system clean [...] ## The Rise of Open-Weight AI Models and the Challenge to Commercial Frontier Labs DevFeed: [The Rise of Open-Weight AI Models and the Challenge to Commercial Frontier Labs]() Original publisher: [Read original article]() Author: Colin Eberhardt Published: 2026-07-27T00:00:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [AI Models](), [Open Source](), [OpenAI](), [anthropic](), [deepseek](), [ChatGPT](), [llama](), [Reinforcement learning](), [Transformer]() Tags: [2026](), [ai](), [ai-models](), [anthropic](), [artificial-intelligence](), [chatgpt](), [china](), [cost](), [deepseek](), [llama](), [open-source](), [openai](), [performance](), [privacy]() ### AI overview This article traces the development of commercial and open-weight AI models from OpenAI's founding through the rise of models predominantly emanating from China. It argues that open-weight models have narrowed or closed the capability gap with frontier labs and may offer comparable or better performance at lower cost, with greater ownership and data privacy. ### Source excerpt Chinese-led open-weight AI models have closed the capability gap with frontier labs like OpenAI and Anthropic, challenging the assumption that massive investments guarantee lasting competitive advantage. By 2026, open-weight alternatives offer equivalent or superior performance at a fraction of the cost, along with greater ownership and data privacy, fundamentally disrupting the commercial AI landscape. ## Native-speed vLLM transformers modeling backend DevFeed: [Native-speed vLLM transformers modeling backend]() Original publisher: [Read original article]() Author: Harry Mellor; Lysandre Published: 2026-07-08T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [vllm](), [Inference Performance](), [Inference](), [LLMs](), [Machine learning](), [Open Source]() Tags: [architecture](), [batching](), [gpu](), [inference](), [inference-performance](), [llama](), [llama-cpp](), [llms](), [mixture-of-experts](), [mlx](), [open-source](), [optimization](), [sglang](), [transformers](), [vllm]() ### AI overview This article describes the transformers modeling backend for vLLM and its role in serving Hugging Face models without requiring model authors to port their implementations. It explains how transformers supplies modeling code while vLLM contributes optimized inference techniques, and outlines comparisons with vLLM's native implementations across several Qwen3 model configurations. It also discusses inference-performance factors including GPU parallelization, compilation, fused kernels, and custom attention kernels. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Using local LLMs for agentic coding DevFeed: [Using local LLMs for agentic coding]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-06-04T09:01:34Z Content type: tutorial Language: en Sources: [Alex Ewerlof Notes]() Topics: [coding](), [agentic-coding](), [Large Language Model](), [GitHub Copilot CLI](), [Linux](), [llama.cpp](), [Ollama](), [NVIDIA RTX]() Tags: [agentic-coding](), [agents](), [ai](), [claude](), [coding](), [github](), [linux](), [llama](), [llama-cpp](), [llms](), [local](), [local-llms](), [microsoft](), [nvidia](), [nvidia-rtx](), [ollama](), [quality](), [rocm](), [state]() ### AI overview A practical guide to using local language models for agentic coding. It covers running local models, configuring coding agents such as Copilot and Pi, and evaluating local models against cloud models in terms of cost, privacy, tooling, and performance. ### Source excerpt AI honeymoon pricing is over, but your work is not ## Reachy Mini goes fully local DevFeed: [Reachy Mini goes fully local]() Original publisher: [Read original article]() Author: Amir Mahla; Andres Marafioti Published: 2026-05-27T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [reachy](), [gemma4](), [llama.cpp](), [Large Language Model](), [speech-to-speech](), [hugging face](), [WebSocket]() Tags: [api](), [artificial-intelligence](), [audio](), [blog](), [gemma](), [guide](), [how-to](), [llama](), [llama-cpp](), [llm](), [local](), [local-ai](), [open-source](), [reachy](), [robotics](), [server](), [speech](), [speech-to-speech]() ### AI overview A tutorial for running fully local conversations with a Reachy Mini robot. It describes a cascaded VAD, speech-to-text, LLM, and text-to-speech pipeline using llama.cpp with Gemma 4, Silero VAD, Parakeet-TDT STT, and Qwen3-TTS, connected through a Realtime API-compatible WebSocket. The setup avoids cloud services, API keys, and sending data off the local machine. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Why and How to Run Local Models in Zed DevFeed: [Why and How to Run Local Models in Zed]() Original publisher: [Read original article]() Author: Cameron Mcloughlin Published: 2026-05-19T00:00:00Z Content type: tutorial Language: en Sources: [Zed Industries - Blog]() Topics: [Local AI](), [LLMs](), [llama.cpp](), [Ollama](), [data]() Tags: [developer](), [hardware](), [llama](), [llama-cpp](), [llms](), [local](), [local-ai](), [ollama]() ### AI overview This article explains why developers may choose local models in Zed, focusing on privacy, cost, control, and availability. It also discusses their limitations compared with cloud-hosted frontier models and introduces setup guidance. ### Source excerpt You can run local AI models in Zed to get better performance and control over your data. Here's how. ## How Replicate Handles Billing: A Complete Breakdown DevFeed: [How Replicate Handles Billing: A Complete Breakdown]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-09T00:00:00Z Content type: article Language: en Sources: [Dodo Payments Blog]() Topics: [AI Platforms/Deployment](), [AI Infrastructure](), [GPU](), [Multi-GPU](), [llama](), [stable-diffusion](), [Whisper]() Tags: [ai](), [ai-infrastructure](), [ai-platform](), [analysis](), [billing](), [compute](), [gpu](), [infrastructure](), [llama](), [models](), [multi-gpu](), [stable-diffusion](), [usage-based-billing](), [whisper]() ### AI overview The article analyzes Replicate's usage-based billing model, which charges for compute time by hardware type rather than by subscription, model, or token package. It explains hardware-tier pricing, multi-GPU committed-spend requirements, and model-agnostic billing, and discusses how to implement similar per-second billing for an AI platform. ### Source excerpt A detailed analysis of Replicate's pure usage-based billing model - per-second compute pricing across hardware tiers, cold start costs, and how to build the same pay-per-second infrastructure billing for your own AI platform. ## Welcome Gemma 4: Frontier multimodal intelligence on device DevFeed: [Welcome Gemma 4: Frontier multimodal intelligence on device]() Original publisher: [Read original article]() Author: merve; Pedro Cuenca; Sergio Paniego; ben burtenshaw; Steven Zheng; Alvaro Bartolome; Nathan Habib Published: 2026-04-02T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [gemma4](), [multimodal](), [Open Source](), [long-context](), [quantization](), [llama.cpp](), [MLX](), [Rust](), [webgpu]() Tags: [audio](), [benchmarks](), [fine-tuning](), [gemma](), [gemma4](), [generation](), [google](), [guide](), [llama](), [llama-cpp](), [local](), [long-context](), [mlx](), [models](), [moe](), [multimodal](), [on-device](), [open](), [open-source](), [quantization](), [science](), [speed]() ### AI overview Gemma 4 is presented as an open, Apache 2-licensed family of multimodal models supporting image, video, text, and, for selected variants, audio inputs. The article highlights on-device deployment, long-context support, compatibility across libraries and devices, quantization, benchmark results, and implementation options including llama.cpp, MLX, WebGPU, and Rust. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Operational Costs and Performance Constraints of Self-Hosting Llama-3 DevFeed: [Operational Costs and Performance Constraints of Self-Hosting Llama-3]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-21T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [llama](), [Inference](), [GPU](), [Large Language Model](), [Latency]() Tags: [cuda](), [gpu](), [inference](), [kv-cache](), [latency](), [llama](), [llm](), [operations](), [vram]() ### 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. ## GTX 1080 Ti for Local LLM DevFeed: [GTX 1080 Ti for Local LLM]() Original publisher: [Read original article]() Published: 2026-02-28T23:33:54Z Content type: article Language: en Sources: [Ariya Hidayat]() Topics: [GPU](), [LLMs](), [llama.cpp](), [Nvidia](), [CUDA](), [Inference Performance](), [Retrieval Augmented Generation (RAG)]() Tags: [cmake](), [command-line](), [cuda](), [gcc](), [gemma](), [gpu](), [inference](), [inference-performance](), [llama](), [llama-cpp](), [llms](), [nvidia]() ### AI overview This article evaluates the eight-year-old NVIDIA GTX 1080 Ti as an affordable option for running local LLMs. It explains how its 11 GB of VRAM can support quantized models and relevant context windows, then describes measuring prompt-processing and token-generation performance with CUDA-enabled llama.cpp. ### Source excerpt Despite being over eight years old, the NVIDIA GTX 1080 Ti remains a compelling choice for enthusiasts keen on running LLM locally. ## 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]() Original publisher: [Read original article]() Author: Hemasumanth Rasineni Published: 2026-02-19T14:42:18Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Inference](), [Digital Ocean](), [Optimization](), [GPU](), [benchmarking](), [Large Language Model](), [llama](), [model-deployment](), [quantization]() Tags: [ai](), [benchmark](), [benchmarking](), [cost](), [digitalocean](), [droplets](), [engineering](), [gpu](), [hardware](), [inference](), [llama](), [optimization](), [quantization](), [running](), [tokens]() ### 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 ## Granite 4.0 Nano: Just how small can you go? DevFeed: [Granite 4.0 Nano: Just how small can you go?]() Original publisher: [Read original article]() Author: Kate Soule; Rameswar Panda Published: 2025-10-28T14:59:38Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [ibm](), [Large Language Model](), [Model Development](), [Open Source Models & Datasets](), [llama.cpp](), [MLX](), [vllm](), [Transformer](), [Loop Engineering](), [gemma](), [qwen](), [Google]() Tags: [apache](), [architecture](), [benchmarks](), [code](), [data](), [development](), [gemma](), [google](), [ibm](), [language-models](), [llama](), [llama-cpp](), [math](), [mlx](), [model](), [model-development](), [models](), [on-device](), [qwen](), [training]() ### AI overview IBM introduces Granite 4.0 Nano, a family of compact language models for edge and on-device applications. The release includes hybrid-SSM and traditional transformer variants ranging from roughly 350M to 1.5B parameters, supports vLLM, llama.cpp, and MLX, and is released under the Apache 2.0 license. The article reports strong performance across knowledge, math, code, safety, instruction-following, and tool-calling benchmarks. ### Source excerpt Today we are excited to share Granite 4.0 Nano, our smallest models yet, released as part of IBM's Granite 4.0 model family. Designed for the edge and on-device applications, these models demonstrate excellent performance for their size and represent IBM's continued commitment to develop powerful, useful, models that don't require hundreds of billions of parameters to get the job done. ## Fine-tune Any LLM from the Hugging Face Hub with Together AI DevFeed: [Fine-tune Any LLM from the Hugging Face Hub with Together AI]() Original publisher: [Read original article]() Author: Zain Hasan; Artem Chumachenko; Egor Timofeev; Max Ryabinin Published: 2025-09-10T17:04:36Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Fine-tuning](), [hugging face](), [Large Language Model](), [model-deployment](), [Inference](), [GPU]() Tags: [ai](), [customization](), [devops](), [fine-tuning](), [gpu](), [hub](), [hugging-face](), [inference](), [llama](), [llm](), [memory](), [meta](), [pipeline]() ### AI overview The article announces an integration that enables compatible LLMs from the Hugging Face Hub to be fine-tuned using Together AI infrastructure. It describes using a Together AI base model as a training template for infrastructure configuration, optimization, GPU and memory allocation, training, and inference, while the custom model is the Hugging Face model being fine-tuned. Models can be trained, deployed for inference, downloaded, or pushed back to the Hub, including support for private repositories with appropriate tokens. ### Source excerpt But here's the challenge: finding an amazing model is just the beginning. What happens when you discover a model that's 90% perfect for your use case, but you need that extra 10% of customization? Traditional fine-tuning infrastructure is complex, expensive, and often requires significant DevOps expertise to set up and maintain. This is exactly the gap that Together AI and Hugging Face are bridging today. ## NVIDIA Releases 6 Million Multi-Lingual Reasoning Dataset DevFeed: [NVIDIA Releases 6 Million Multi-Lingual Reasoning Dataset]() Original publisher: [Read original article]() Author: Jane Polak Scowcroft; Dhruv Nathawani; Shuoyang Ding; Oleksii Kuchaiev; Vitaly Lavrukhin Published: 2025-08-20T22:13:18Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [datasets](), [Nemotron](), [post-training](), [Open Source Models & Datasets](), [Mamba](), [Transformer](), [Large Language Model](), [Nvidia]() Tags: [datasets](), [japanese](), [llama](), [mamba](), [model-development](), [nemotron](), [nvidia](), [open](), [post-training](), [reasoning]() ### AI overview NVIDIA announces a 6-million-example multilingual reasoning dataset translated into French, Spanish, German, Italian, and Japanese. The article also presents Nemotron Nano 2 9B, an edge-oriented model using a hybrid Transformer-Mamba architecture, configurable thinking budgets, and open model weights and training resources. ### Source excerpt NVIDIA continues releasing permissive datasets in support of the open ecosystem with 6 Million Multilingual Reasoning Dataset. Continuing the success of the recent Nemotron Post-Training Dataset v1 release used in Llama Nemotron Super model, and our Llama Nemotron Post-Training Dataset release earlier this year, we're excited to release the reasoning dataset translated into five target languages: French, Spanish, German, Italian, and Japanese. ## Measuring Open-Source Llama Nemotron Models on DeepResearch Bench DevFeed: [Measuring Open-Source Llama Nemotron Models on DeepResearch Bench]() Original publisher: [Read original article]() Author: Jay Rodge Published: 2025-08-04T19:51:50Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Nvidia](), [Open Source](), [hugging face](), [llama](), [Nemotron](), [Large Language Model](), [Open Source Models & Datasets](), [long-context](), [Inference](), [post-training](), [GPU](), [Reinforcement learning]() Tags: [agentic](), [ai](), [gpu](), [hallucination-detection](), [hugging-face](), [inference](), [llama](), [llm](), [long-context](), [nemotron](), [nvidia](), [open-source](), [post-training](), [reinforcement-learning]() ### 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? ## Ettin Suite: SoTA Paired Encoders and Decoders DevFeed: [Ettin Suite: SoTA Paired Encoders and Decoders]() Original publisher: [Read original article]() Author: Orion Weller; K Ricci; Marc Marone; Antoine Chaffin; Dawn Lawrie; Ben Van Durme Published: 2025-07-16T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [Machine Learning & Artificial Intelligence](), [Open Source](), [Large Language Model](), [Model Development](), [datasets]() Tags: [artificial-intelligence](), [bert](), [community](), [comparisons](), [datasets](), [embedding](), [llama](), [llm](), [model-development](), [nlp](), [open-source](), [open-source-collab](), [qwen](), [research](), [retrieval](), [training]() ### AI overview The article introduces Ettin, a suite of paired encoder-only and decoder-only language models ranging from 17M to 1B parameters. The models are trained with identical data, architectures, and recipes, enabling controlled comparisons between masked and causal language modeling. Ettin reports state-of-the-art performance for open-data models and explores converting models between encoder and decoder architectures. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Creating custom kernels for the AMD MI300 DevFeed: [Creating custom kernels for the AMD MI300]() Original publisher: [Read original article]() Author: Rémi Ouazan Reboul; seungrok jung Published: 2025-07-09T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [kernels](), [GPU](), [vllm](), [hugging face](), [model-deployment](), [Optimization](), [llama](), [Open Source]() Tags: [amd](), [cuda](), [gpu](), [hugging-face](), [inference](), [kernels](), [latency](), [llama](), [open-source](), [optimization](), [vllm]() ### 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. [Next page]()