# LLM Techniques A technical topic covering techniques for training, optimizing, and serving large language models. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Value Induction Reshapes LLM Behaviour DevFeed: [How Value Induction Reshapes LLM Behaviour]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [LLM Techniques](), [LLM evaluation / benchmarking]() Tags: [complex](), [effect](), [language-models](), [llm](), [safety](), [values]() ### AI overview This research examines how inducing selected values in conversational large language models affects other values, safety, anthropomorphic language, and question-answering benchmark performance. It finds that value induction can affect related and contrastive values, positive values can increase safety, and all tested values increased anthropomorphic, validating, and sycophantic language. ### Source excerpt Conversational Large Language Models are post-trained on language that expresses specific behavioural traits, such as curiosity, open-mindedness, and empathy, and values, such as helpfulness, harmlessness, and honesty. This is done to increase utility, ensure safety, and improve the experience of the people interacting with the model. However, values are complex and inter-related - inducing one could modify behaviour on another. Further, inducing certain values can make models more addictive or sycophantic through language used in the generations, with a potential detrimental effect on the... ## DACA-GRPO: Denoising-Aware Credit Assignment for Reinforcement Learning in Diffusion Language Models DevFeed: [DACA-GRPO: Denoising-Aware Credit Assignment for Reinforcement Learning in Diffusion Language Models]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [grpo](), [Reinforcement learning](), [large-language-models](), [LLM Techniques](), [Code]() Tags: [code-generation](), [denoising](), [diffusion](), [grpo](), [json-schema](), [optimization](), [reinforcement-learning]() ### AI overview The article introduces DACA-GRPO, a denoising-aware enhancement for GRPO-style reinforcement-learning trainers used with diffusion language models. It uses per-token denoising progress scores and stratified masking likelihood to improve credit assignment and reduce bias in likelihood estimates. Across seven benchmarks, the method reports gains in mathematical reasoning, code generation, constraint satisfaction, and JSON Schema adherence. ### Source excerpt Diffusion large language models are a compelling alternative to autoregressive models, yet existing RL methods for diffusion treat all denoising steps as equally important and rely on biased, high-variance likelihood estimates. We identify two fundamental weaknesses: the absence of temporal credit assignment across the denoising trajectory, and the systematic bias of mean-field likelihood estimates used for policy optimization. To address these, we propose Denoising-Aware Credit Assignment for GRPO (DACA-GRPO), a lightweight, plug-and-play enhancement for any GRPO-style trainer. DACA-GRPO... ## ToolGrad: Efficient tool-use dataset generation with textual "gradients" DevFeed: [ToolGrad: Efficient tool-use dataset generation with textual "gradients"]() Original publisher: [Read original article]() Published: 2026-09-10T22:50:22Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [dataset](), [Large Language Model](), [LLM Techniques](), [Machine learning](), [Fine-tuning](), [LLM evaluation / benchmarking]() Tags: [agent](), [cost](), [datasets](), [evaluation](), [fine-tuning](), [generation](), [llm](), [machine-intelligence](), [machine-learning](), [natural-language-processing](), [prompt-engineering](), [tool]() ### AI overview ToolGrad generates tool-use chains before deriving corresponding user queries, aiming to create complex training data for LLM tool use more efficiently and at lower cost than exploration-based approaches. ### Source excerpt Machine Intelligence ## On-Policy Distillation, Simply Explained DevFeed: [On-Policy Distillation, Simply Explained]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-09-09T19:22:38Z Content type: tutorial Language: en Sources: [Into AI]() Topics: [LLM Techniques](), [Large Language Model](), [post-training](), [Reinforcement learning]() Tags: [fine-tuning](), [gpu](), [llms](), [post-training](), [training]() ### AI overview This tutorial explains on-policy distillation (OPD), a post-training method for large language models. A student model generates its own responses, which a stronger teacher scores token by token; the student is trained to reduce the reverse KL divergence between their next-token distributions. The article distinguishes OPD from reinforcement learning, conventional knowledge distillation, and supervised fine-tuning. ### Source excerpt On-Policy Distillation (OPD) has become a popular algorithm for post-training LLMs, and almost all recent open-weight LLMs (Qwen3, GLM-5.3, and Nemotron-Cascade 2) have used it to achieve amazing performance. ## 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. ## How we think about text classification in the LLM era DevFeed: [How we think about text classification in the LLM era]() Original publisher: [Read original article]() Author: Raphael Montaud Published: 2026-08-19T20:00:37Z Content type: article Language: en Sources: [Medium]() Topics: [Machine Learning & Artificial Intelligence](), [Model Development](), [LLM Techniques](), [Artificial Intelligence](), [AI Inference]() Tags: [ai](), [classification](), [inference](), [llm](), [llms](), [machine-learning](), [model](), [models](), [recommendation-system](), [text-classification]() ### AI overview Medium explains how it is evaluating LLM-based text classification for updating its aging NSFW model while retaining task-specific machine-learning models. The article states that Snowflake LLM tools were used for inference only and that Medium's user data was not used to train the models. ### Source excerpt Why we think LLMs can be useful and why we will not replace all of our models with themContext At Medium, we have many Machine Learning models that we use to label stories automatically. These affect what stories we recommend to readers. Here's some examples: a few of our text classification models. All diagrams and charts made by the authorSome Clarifications on our Machine Learning policy Before we go deep on this project, I just wanted to clarify a few things about how we stand regarding AI in general. Medium has been training internal models with user and post data for a long time now. We train models with specific tasks. For example, models that power our recommendations algorithm, or text classification models like the ones presented in this story. All in the goal to improve our product. With the LLM approach I describe in this story, we ARE NOT sharing these models with other companies. And we ARE NOT allowing anyone to train on our users' data and content. Here we used Snowflake LLM tools for inference only (no LLM training was done here) and they are actually hosting all of the models inside their own infrastructure and guarantee that they are not using any of this for training. Shoutout to the Snowflake team for making it so easy and safe to use LLMs on our data! If you want to read more about Medium's stance on AI, I definitely recommend giving these a read: Default No to AI Training on Your Stories Finally, an internet standard for writers' rights vs. AI companies We want your feedback: How can writers use AI to tell human stories? Problem During our roadmap planning we decided that our NSFW model was out of date and it was time to revamp it. This model labels stories as "Not Safe for Work" if they have sexually explicit content, lots of profanity, or basically anything you wouldn't want to read on your big monitor in the middle of an open space! As you can imagine it's a pretty important model. We really need it to make sure our most "interesting" conte ## Making Knowledge Distillation Cheap Enough to Run at Scale DevFeed: [Making Knowledge Distillation Cheap Enough to Run at Scale]() Original publisher: [Read original article]() Author: Antonio Tiene; Iker García-Ferrero; Ali Hashemi; Bakbergen Ryskulov Published: 2026-08-10T10:05:36Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [LLM Techniques](), [Large Language Model](), [Post-training optimization](), [Model Routing](), [GPU](), [Machine learning](), [PyTorch](), [gpt-oss](), [Nvidia]() Tags: [blog](), [caching](), [compute](), [cost](), [gpt](), [gpt-oss](), [gpu](), [hugging-face](), [language-models](), [large-language-models](), [long-context](), [machine-learning](), [memory](), [performance](), [pytorch](), [qwen](), [research](), [training]() ### AI overview This article presents an efficient approach to knowledge distillation for large language models. It caches the teacher model's top-K logits offline and uses a fused, memory-efficient chunked KL-divergence loss, avoiding simultaneous teacher-student residency and full vocabulary-by-sequence-length matrices. The changes reduce VRAM use and training cost, enabling long-context distillation on a single GPU and making larger-scale experimentation more practical. ### Source excerpt A Blog post by Multiverse Computing on Hugging Face ## GenRec: Towards LLM-Native Recommendation at Netflix DevFeed: [GenRec: Towards LLM-Native Recommendation at Netflix]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-07-30T20:10:15Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [Netflix](), [recommendation systems](), [Large Language Model](), [LLM Techniques](), [personalization](), [vllm]() Tags: [data](), [feature-engineering](), [genai](), [large-language-models](), [large-language-models-llms](), [llm](), [llms](), [netflix](), [personalization](), [ranking](), [recommendation-system](), [vllm]() ### AI overview Netflix presents GenRec, an LLM-backed recommendation ranker trained on Netflix-specific data and objectives. It converts user histories, item metadata, and context into text, uses a catalog-aware scoring head, aligns recommendations with long-term member value and business goals, and runs in prefill-only mode on Netflix's LLM serving stack. In a large-scale A/B test, GenRec improved short- and long-term online metrics while using fewer labeled examples and input signals than a mature production ranker. ### Source excerpt Authors: Ying Li, Arjun Rao, Shradha Sehgal Introduction Recommendations sit at the heart of the Netflix experience. Our current production models rely on thousands of hand-crafted features over users, items, and interactions, along with specialized architectures for sequence modeling, feature interactions, and multi-task objectives. This stack has evolved over many years to support diverse content types (movies, series, games, live, podcasts) and product surfaces, but its complexity makes it costly to onboard new use cases: adding a content type or surface can require significant feature engineering, architecture change, infrastructure work, and experimentation. At the same time, large language models (LLMs) are changing how we think about recommendation, as shown by recent work such as PLUM, GLIDE, and OneRec-Think. Their broad world knowledge and strong language understanding make it possible to represent user histories and item metadata directly as text, capture rich relationships in a shared semantic space, and steer recommendations via natural-language prompts. However, off-the-shelf LLMs are still far from production-ready recommenders: they often over-recommend globally popular content, hallucinate out-of-catalog items, ignore business constraints, and provide only limited personalization. To address this, we built GenRec, an LLM-backed recommendation ranker that post-trains an internal foundation LLM on Netflix-specific data and objectives. GenRec shows that an LLM-based ranker can match or exceed a mature production system while relying on far fewer labeled examples and input signals. Figure 1: GenRec pipeline. Raw logs of user history, item metadata, and context are transformed via context engineering into natural-language prompts and fed into the GenRec, which runs on vLLM in prefill-only mode and outputs scores for each catalog item, yielding a recommendation ranking. At a high level, GenRec: Verbalizes user histories, item metadata, and context as text ## EvoLib: Turning experience into evolving knowledge DevFeed: [EvoLib: Turning experience into evolving knowledge]() Original publisher: [Read original article]() Author: Weijia Xu, Zelalem Gero, Michel Galley, Eric Yuan, Jianfeng Gao Published: 2026-07-30T16:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [Large Language Model](), [AI Models](), [Language models](), [AI Agent](), [LLM Techniques](), [model-deployment]() Tags: [agent](), [agents](), [ai](), [ai-models](), [inference](), [language-models](), [learning](), [llms](), [memory](), [performance](), [post](), [reasoning](), [research](), [research-blog]() ### AI overview EvoLib is a framework for turning an AI system's past attempts into reusable skills and reflective insights. It continually refines, consolidates, and reweights this evolving knowledge so models can learn from successes and failures across tasks without updating the underlying model. ### Source excerpt LLMs do not get smarter just by remembering more. EvoLib turns experience into evolving knowledge, taking reusable skills and insights that help models learn and adapt across tasks long after deployment. The post EvoLib: Turning experience into evolving knowledge appeared first on Microsoft Research. ## Reducing High-Bandwidth Memory Bottlenecks in JAX-Based LLM Training with Host Offloading DevFeed: [Reducing High-Bandwidth Memory Bottlenecks in JAX-Based LLM Training with Host Offloading]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-10T18:17:40Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [LLM Techniques](), [Large Language Model](), [GPU](), [Mixture of Experts (MoE)](), [cpu](), [NVLink](), [systems](), [Nvidia]() Tags: [agentic-ai-generative-ai](), [blackwell](), [compute](), [cpu](), [developer-tools-techniques](), [featured](), [gb200](), [gpu](), [grace-cpu](), [large-language-model](), [llm](), [llm-techniques](), [mixture-of-experts-moe](), [nvidia](), [nvl72](), [nvlink](), [simulation-modeling-design](), [training-ai-models](), [vera-rubin](), [work]() ### AI overview This article explains how host offloading in JAX-based large language model training reduces GPU high-bandwidth memory pressure by moving selected activations to pinned host memory and streaming them back during the backward pass. It discusses activation-transfer overlap, NVIDIA Grace Blackwell and GB200 NVL72 systems, and experiments involving Llama 3.1 405B and DeepSeek-V3 671B. ### Source excerpt Large language model (LLM) training workloads increasingly run into GPU memory limits before compute is fully used. Model weights, gradients, optimizer states,... ## Enhancing Goodput in Large-Scale LLM Training with Nonuniform Tensor Parallelism DevFeed: [Enhancing Goodput in Large-Scale LLM Training with Nonuniform Tensor Parallelism]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-06T21:44:23Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [Large Language Model](), [Training AI Models](), [LLM Techniques](), [NVLink](), [Availability](), [Hardware](), [Network](), [systems](), [Nvidia]() Tags: [ai](), [ai-training](), [availability](), [blackwell](), [data-center-cloud](), [developer-tools-techniques](), [efficiency](), [featured](), [gpu](), [hardware](), [llm](), [llm-techniques](), [mlops](), [network](), [nvidia](), [nvidia-blackwell](), [nvlink](), [performance](), [scale](), [training](), [work]() ### AI overview This article explains how Nonuniform Tensor Parallelism can improve Goodput in large-scale LLM training by adapting tensor parallelism to changing GPU availability and overlapping data resharding. The experimental approach aims to reduce interruptions, lost throughput, and computational waste in tightly interconnected GPU clusters. ### Source excerpt Training LLMs at massive scale brings unique infrastructure challenges, especially as jobs span thousands of GPUs and run for extended periods. The longer these... ## How LLMs are Actually Trained DevFeed: [How LLMs are Actually Trained]() Original publisher: [Read original article]() Author: Ashish Pratap Singh Published: 2026-06-11T04:50:19Z Content type: tutorial Language: en Sources: [AlgoMaster Newsletter]() Topics: [Training AI Models](), [LLM Techniques](), [datasets](), [Language models]() Tags: [dataset](), [llm](), [llm-training](), [llms](), [models](), [training](), [training-data]() ### AI overview This tutorial explains how large language models are trained in stages: pretraining, mid-training, and post-training. It describes pretraining datasets, next-token prediction, cross-entropy loss, and the creation and filtering of datasets such as Dolma 3 Mix. ### Source excerpt In the last lesson, we learned how the Transformer architecture powers an LLM. ## Cooking with constraints: A designer's framework for better AI prompts DevFeed: [Cooking with constraints: A designer's framework for better AI prompts]() Original publisher: [Read original article]() Author: Greg Huntoon Published: 2026-01-05T06:00:00Z Content type: tutorial Language: en Sources: [Figma Blog]() Topics: [Prompt Engineering](), [LLM Techniques](), [Language models](), [Framework]() Tags: [ai](), [design](), [designer](), [framework](), [language-models](), [llms](), [prompt](), [prompting]() ### AI overview This article presents a framework for writing better AI prompts in design work. It argues that clear instructions, context, and constraints help reduce ambiguity and make probabilistic model outputs more structured, repeatable, and intentional. ### Source excerpt Design and cooking share a truth: Preparation determines the outcome. Structured prompts turn AI from guesswork into a reliable design partner. ## Tokenization in Transformers v5: Simpler, Clearer, and More Modular DevFeed: [Tokenization in Transformers v5: Simpler, Clearer, and More Modular]() Original publisher: [Read original article]() Author: Ita Zaporozhets; Aritra Roy Gosthipaty; Arthur Zucker; Sergio Paniego; merve; Pedro Cuenca Published: 2025-12-18T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Language models](), [LLM Techniques](), [AI, ML & Data Engineering]() Tags: [algorithms](), [architecture](), [backend](), [language-models](), [large-language-models-llms](), [natural-language-processing](), [open-source](), [rust](), [tokenization](), [tokenizers](), [training](), [transformers](), [v5]() ### AI overview A practical guide to the Transformers v5 tokenizer redesign. It explains tokenization for LLMs, separates tokenizer architecture from trained vocabulary, and covers modular processing stages and algorithms such as BPE. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## T5Gemma: A new collection of encoder-decoder Gemma models DevFeed: [T5Gemma: A new collection of encoder-decoder Gemma models]() Original publisher: [Read original article]() Author: Biao Zhang; Paul Suganthan; Ben Hora Published: 2025-10-25T18:14:00Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [gemma](), [Large Language Model](), [Transformer](), [LLM Techniques](), [LLM evaluation / benchmarking]() Tags: [architecture](), [benchmarks](), [gemma](), [inference](), [inference-efficiency](), [llms](), [models](), [performance](), [research](), [training]() ### AI overview The article introduces T5Gemma, a collection of encoder-decoder large language models created by adapting pretrained decoder-only Gemma 2 models. It describes pretrained and instruction-tuned variants, flexible encoder-decoder configurations, and reported quality and inference-efficiency advantages across benchmarks such as SuperGLUE. ### Source excerpt Introducing T5Gemma, a new collection of encoder-decoder LLMs. ## Scaling LLM Inference: Innovations in Tensor Parallelism, Context Parallelism, and Expert Parallelism DevFeed: [Scaling LLM Inference: Innovations in Tensor Parallelism, Context Parallelism, and Expert Parallelism]() Original publisher: [Read original article]() Author: Cen Zhao; Xiaodong Wang; Jianyu Huang Published: 2025-10-17T16:00:50Z Content type: article Language: en Sources: [Meta AI Research]() Topics: [Large Language Model](), [AI Inference](), [Inference Performance](), [LLM Techniques](), [GPU](), [sharding](), [long-context]() Tags: [ai-research](), [gpu](), [inference](), [kv-cache](), [large-language-models-llms](), [latency](), [llms](), [parallelism](), [performance](), [sharding]() ### AI overview Meta describes three forms of parallelism--tensor, context, and expert parallelism--for scaling large language model inference across GPUs. The article explains how prefill and decoding differ computationally and how these techniques target resource efficiency, throughput, and latency. ### Source excerpt At Meta, we are constantly pushing the boundaries of LLM inference systems to power applications such as the Meta AI App. We're sharing how we developed and implemented advanced parallelism techniques to optimize key performance metrics related to resource efficiency, throughput, and latency. The rapid evolution of large language models (LLMs) has ushered in a [...] Read More... The post Scaling LLM Inference: Innovations in Tensor Parallelism, Context Parallelism, and Expert Parallelism appeared first on Engineering at Meta. ## Whodunit: LLM Murder Mysteries DevFeed: [Whodunit: LLM Murder Mysteries]() Original publisher: [Read original article]() Author: James Heller Published: 2025-08-26T07:00:00Z Content type: article Language: en Sources: [Apartment 304]() Topics: [LLM Techniques](), [Go Language]() Tags: [ai-authored](), [apartment-304](), [custom-software-solutions](), [devops](), [devops-engineer](), [go](), [golang](), [language-models](), [large-language-models](), [llm](), [logic](), [models](), [puzzle](), [puzzles](), [software-architecture](), [software-development](), [software-engineer](), [strategies](), [test]() ### AI overview This article describes building Whodunit, a low-tech murder-mystery game whose mysteries are written with large language models. It discusses developing a mental model for calling LLMs, testing LLM services and Go libraries, improving logical soundness through trial and error, and managing unreliable LLM APIs. ### Source excerpt Can Large Language Models write engaging mysteries? Find out in this week's episode of "Whodunit?" ## Falcon-Edge: A series of powerful, universal, fine-tunable 1.58bit language models. DevFeed: [Falcon-Edge: A series of powerful, universal, fine-tunable 1.58bit language models.]() Original publisher: [Read original article]() Author: Younes B; Qiyang Zhao; Hang Zou; Rhaiem; Ilyas Chahed; Maksim Velikanov; Jingwei Zuo; Mike Lubinets; Hakim Hacid; Falcon LLM TII UAE Published: 2025-05-15T13:13:45Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Large Language Model](), [LLM Techniques](), [quantization](), [Compression](), [Fine-tuning](), [Training AI Models](), [hugging face](), [AI Inference]() Tags: [compression](), [fine-tuning](), [floating-point](), [hugging-face](), [language-models](), [precision](), [quantization](), [train](), [training]() ### AI overview The article introduces Falcon-Edge, a series of 1.58-bit language models based on the BitNet architecture. The models use ternary weights during training to reduce memory use and improve deployment efficiency, and are offered in 1-billion- and 3-billion-parameter base and instruction-tuned variants. The article describes their training approach, fine-tuning variants, and evaluation on the former Hugging Face leaderboard v2 benchmark. ### Source excerpt A Blog post by Technology Innovation Institute on Hugging Face ## Mastering Long Contexts in LLMs with KVPress DevFeed: [Mastering Long Contexts in LLMs with KVPress]() Original publisher: [Read original article]() Author: Simon Jegou; Maximilian Jeblick Published: 2025-01-23T08:03:03Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Large Language Model](), [Compression](), [long-context](), [Cache](), [LLM Techniques](), [text-generation](), [model architecture]() Tags: [architecture](), [cache](), [compression](), [context-window](), [language-models](), [llms](), [long-context](), [model](), [model-architecture](), [models](), [text-generation](), [tokens]() ### AI overview This article introduces KVPress, an NVIDIA toolkit that applies KV cache compression techniques to make long-context Large Language Models (LLMs) more memory-efficient. It explains how context windows enable in-context retrieval, learning, and extended reasoning, and why KV Cache memory usage grows with context length. The article also describes how KV Cache reuses attention-layer keys and values during autoregressive text generation. ### Source excerpt TL;DR: KVPress packs the latest KV cache compression techniques, enabling memory-efficient long-context LLMs. 🚀 One of the key features of Large Language Models (LLMs) is their context window--the maximum number of tokens they can process in a single request. As LLMs evolve, their context windows are becoming increasingly larger. Larger context windows unlock incredible possibilities: - In-context retrieval: Seamlessly referencing large amounts of text within a single query. ## How LinkedIn Reduced GPU Memory Usage by 60% for LLM Training DevFeed: [How LinkedIn Reduced GPU Memory Usage by 60% for LLM Training]() Original publisher: [Read original article]() Author: Richard Oliver Bray Published: 2025-01-16T15:19:14Z Content type: tutorial Language: en Sources: [Hacking Scale by Better Stack]() Topics: [Large Language Model](), [GPU optimization](), [GPU](), [Library](), [LLM Techniques]() Tags: [gpu](), [library](), [llm](), [memory](), [performance](), [techniques](), [training]() ### AI overview This article explains how LinkedIn improved the efficiency of training large language models. It describes the resource demands of pre-training and reports that LinkedIn reduced GPU memory usage by 60% by applying selected GPU performance techniques in a library. ### Source excerpt LinkedIn hand-picked the best GPU performance techniques and put them in a library ## Entropix dynamically adapts LLM execution strategies based on model signals DevFeed: [Entropix dynamically adapts LLM execution strategies based on model signals]() Original publisher: [Read original article]() Published: 2024-10-20T00:00:00Z Content type: opinion Language: en Sources: [Tim Kellogg]() Topics: [LLM Techniques](), [Automated prompt engineering](), [Large Language Model](), [Chain-of-thought](), [Reinforcement learning](), [JIT]() Tags: [hallucinations](), [llm](), [llm-techniques](), [prompt-engineering](), [reinforcement-learning]() ### AI overview The article explains entropix as a proposed framework for LLM execution that uses signals from a model's internal state to switch dynamically among techniques such as chain-of-thought prompting and parameter adjustments. It presents this as an approach to automate some prompt-engineering decisions and notes that the current implementation relies on crude heuristics, with reinforcement learning being explored as a replacement. ### Source excerpt The buzz is there! The cooks are cooking! But what is entropix? I wrote a fluff piece here explaining it, but the most comprehensive and complete description of entropix is here. ## Build, tweak, repeat DevFeed: [Build, tweak, repeat]() Original publisher: [Read original article]() Published: 2024-08-07T14:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [Language models](), [LLM Techniques](), [SDKs](), [AI, ML & Data Engineering]() Tags: [agents](), [api](), [fine-tuning](), [language-models](), [python](), [release](), [typescript]() ### AI overview Mistral announces model customization through prompting and fine-tuning, an alpha Agents feature for instruction-driven workflows, and version 1.0 of its Python and TypeScript client SDK. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Benchmarking Text Generation Inference DevFeed: [Benchmarking Text Generation Inference]() Original publisher: [Read original article]() Author: Derek Thomas Published: 2024-05-29T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [benchmarking](), [tgi](), [Inference](), [LLM Techniques](), [model-deployment](), [Retrieval Augmented Generation (RAG)](), [Open Source]() Tags: [benchmarking](), [deployment](), [guide](), [inference](), [llm](), [nlp](), [open-source](), [performance](), [rag](), [techniques](), [tgi](), [watermarking]() ### AI overview This article explains how to benchmark and profile Text Generation Inference (TGI) for different large language model workloads and hardware configurations. It discusses serving optimizations and contrasts the context-window demands of retrieval-augmented generation with basic chat. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. [Next page]()