New KV cache compaction slashes LLM memory use 50× and unlocks chunked long‑context processing for Llama 3.1, Qwen‑3 and beyond. Think faster inference on enterprise datasets—read the full dive! #KVCache #LLMMemory #LongContexts

🔗 aidailypost.com/news/kv-cach...

The Hidden Engineering Behind Fast AI: How LLM Inference Actually Works A deep dive into PagedAttention, speculative decoding, FlashAttention, and continuous batching — the clever tricks that make modern LLMs respond in milliseconds instead of minutes.

The Hidden Engineering Behind Fast AI: How LLM Inference Actually Works

techlife.blog/posts/llm-in...

#LLM #Inference #PagedAttention #vLLM #FlashAttention #SpeculativeDecoding #MachineLearning #GPUOptimization #KVCache

𝗧𝗲𝗻𝘀𝗼𝗿𝗺𝗲𝘀𝗵: 𝗙𝗿𝗼𝗺 𝗔𝗰𝗮𝗱𝗲𝗺𝗶𝗮 𝘁𝗼 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻

In this clip, our 𝗖𝗘𝗢 𝗮𝗻𝗱 𝗰𝗼-𝗳𝗼𝘂𝗻𝗱𝗲𝗿, Junchen Jiang, explains what it really takes to 𝗯𝘂𝗶𝗹𝗱 a 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 at the intersection of academia, open source , and industry.

🎥 Watch the full interview :
👉 y2u.be/zHW4Zzd7pjI

#AIInfrastructure #KVCache #Tensormesh #LLMs

NVIDIA Unveils the Inference Context Memory Storage Platform — A New Era for Long-Context AI NVIDIA’s Inference Context Memory Storage Platform redefines AI memory architecture, enabling long-context inference with HBM4, BlueField-4 DPUs, and Spectrum-X networking. Learn how this shift impact...

NVIDIA’s new ICMSP reshapes AI inference by treating KV cache as a multi-tier memory hierarchy—from HBM to NVMe SSD.
www.buysellram.com/blog/nvidia-...
#NVIDIA #Rubin #AI #Inference #LLM #AIInfrastructure #MemoryHierarchy #HBM #NVMe #DPU #BlueField4 #AIHardware #GPU #DRAM #KVCache #DataCenter #tech

𝗞𝗩 𝗖𝗮𝗰𝗵𝗲: 𝗧𝗵𝗲 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗣𝗶𝗲𝗰𝗲 ...

In this clip, Our 𝗖𝗘𝗢 and 𝗰𝗼-𝗳𝗼𝘂𝗻𝗱𝗲𝗿, 𝗝𝘂𝗻𝗰𝗵𝗲𝗻 𝗝𝗶𝗮𝗻𝗴 , reflects on the moment it clicked that 𝗞𝗩 𝗰𝗮𝗰𝗵𝗶𝗻𝗴 𝘄𝗮𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮𝗻 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻. 𝗕𝘂𝘁 𝗮 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝗵𝗶𝗳𝘁 in how LLM inference should work.

🎥 Watch the full interview on YouTube:
👉 y2u.be/zHW4Zzd7pjI #KVCache

New interview: Tensormesh CEO, co-founder, and UChicago CS Associate Professor Junchen Jiang on why KV cache—the memory of LLMs—is becoming core inference infrastructure. Watch: http://y2u.be/zHW4Zz #LLMInference #KVCache

In this new interview, our CEO & co-founder @JunchenJiang explains why KV cache — the internal memory of LLMs, is becoming the 𝗻𝗲𝘅𝘁 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 layer for AI, and how @tensormesh tackles large-scale inference.

🎥 Watch the full interview: youtu.be/zHW4Zzd7pjI

#LLMInference #KVCache #OpenSource #PyTorch

kv_cache Explained: How It Enhances vLLM Inference - Cloudthrill This blog is my attempt to break it down simply, without drowning in dark math :). If you’ve ever wondered what kv_cache actually does, you’re in the right place. Let’s make it click. explore how KV cache enhances vllm inference

🏆And our #1 - 2025 blog-post on @Cloudthrill is… KV Cache explained (𝗟𝗶𝗸𝗲 𝗜’𝗺 𝟱)

Ever wondered what #KVCache really is in LLM inference?
Here's the simplest analogy for beginners plus an overview of popular KV cache optimization techniques!

📖 cloudthrill.ca/kv_cache-exp...

Do you want to compare the caching performance of your LLM serving stack? We've put together a simple command line tool to do so. Introducing Tensormesh Benchmark.
tensormesh.ai/blog-posts/t...

#llm #ai #kvcache #lmcache #vllm #benchmarking

AI Challenges of KV Cache Compression in Large Language Models

Research on 30 Sep 2025 finds KV cache compression in language models can cause instruction ignoring and leak system prompts; adjusting eviction policies to keep early prompts is suggested. Read more: getnews.me/ai-challenges-of-kv-cach... #kvcache #llm

SemShareKV Boosts LLM Inference with Semantic KV‑Cache Sharing

SemShareKV lets LLMs reuse KV cache entries across semantically similar prompts, cutting inference time by up to 6.25× and GPU memory use by 42% on inputs of up to 5 000 tokens. Read more: getnews.me/semsharekv-boosts-llm-in... #semsharekv #kvcache #llm

KV Cache Steering Enables Chain-of-Thought Reasoning in Frozen LLMs

Cache steering tweaks the KV cache of frozen LLMs in one step, nudging clearer chain‑of‑thought reasoning and lowering latency, with higher accuracy on GPQA and MATH benchmarks. getnews.me/kv-cache-steering-enable... #kvcache #chainofthought

Bottlenecked Transformers Consolidate KV Cache to Improve Reasoning

Bottlenecked Transformer adds a periodic KV‑cache consolidation step, boosting multi‑step reasoning. On math benchmarks it beats a vanilla transformer by up to 6.6 pp. Read more: getnews.me/bottlenecked-transformer... #bottleneckedtransformer #kvcache

OjaKV Enables Online Low‑Rank KV Cache Compression for Long‑Context LLMs

OjaKV compresses KV cache, allowing a 32K-token prompt on Llama‑3.1‑8B (batch 4) to use ~16 GB while keeping zero‑shot accuracy, with the low‑rank subspace updated via Oja’s rule. Read more: getnews.me/ojakv-enables-online-low... #ojakv #kvcache

UNComp Leverages Matrix Entropy for Adaptive LLM Cache Compression

UNComp compresses LLM KV caches to 4.74% of their original size and boosts inference throughput overall by 6.4×, while giving a modest 6% prefill speedup. Read more: getnews.me/uncomp-leverages-matrix-... #kvcache #entropy #llm

Neural Attention Search Reduces Transformer KV Cache for AI Models

NAtS lets transformer models drop less‑important tokens, shrinking the KV cache and cutting memory use and inference cost while keeping perplexity and accuracy unchanged. Read more: getnews.me/neural-attention-search-... #neuralattentionsearch #kvcache

EpiCache Boosts Long Conversational QA Accuracy with KV Management

EpiCache’s training‑free framework improves long‑form conversational QA accuracy by up to 40% and cuts memory use by up to 3.5× while reducing latency by up to 2.4×. Read more: getnews.me/epicache-boosts-long-con... #epicache #kvcache #longconvqa

vLLM production-stack: LLM inference for Enterprises (part1) - Cloudthrill vLLM Production Stack tackles usual issues that come with scaling LLM serving (slow recovery, High GPU bills) with a community-maintained layer that wraps vanilla vLLM, adds a Python-native router, LMCache-powered KV-cache network, autoscaling hooks and Grafana dashboards—all deployable in a single Helm chart. Let's dive into it!✍🏻

🚀#NewBlog #vLLM
📖 𝐯𝐋𝐋𝐌 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐬𝐭𝐚𝐜𝐤: AI inference for enterprises💫

🏢Production-stack is the K8s-native, enterprise-ready inference setup that supercharges vLLM inference at scale, across Clouds.

👉Start here: cloudthrill.ca/vllm-product...

#AI #LLM #vLLM #Kubernetes #MLOps #KVCache #LMCache

Value‑Guided KV Cache Compression Boosts LLM Efficiency with CUR

CurDKV, a KV cache compression method, boosted accuracy by up to 9.6% over SnapKV and ChunkKV and cut generation latency by up to 40% in tests on LLaMA and Mistral models. Read more: getnews.me/value-guided-kv-cache-co... #kvcache #llmefficiency

LAVa Introduces Dynamic Layer‑Wise KV Cache Eviction for LLMs

LAVa introduces KV‑cache compression that dynamically allocates memory across layers and heads, avoiding extra fine‑tuning. Tests on LongBench and InfiniteBench show it beats static baselines. Read more: getnews.me/lava-introduces-dynamic-... #lava #kvcache

#NewBlog 𝐊𝐕 𝗖𝗮𝗰𝗵𝗲 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱: like I'm 5😎
🧠Ever wondered what #KVCache really is in LLM inference? Forget the math-heavy blabla—this one's made to click !
👉check it out: cloudthrill.ca/kv_cache-exp...
@Cloud_Thrill
#vLLM #AIInfra #lmcache

Scaling AI Smarter: NAMMs Revolutionize Transformer Performance Researchers at Sakana AI introduced Neural Attention Memory Models (NAMMs), optimizing transformer efficiency and performance by dynamically managing memory with evolutionary techniques. NAMMs achieve...

Scaling AI Smarter: NAMMs Revolutionize Transformer Performance 🔬✨🚀 www.azoai.com/news/2024121... #AI #Transformers #NAMMs #MachineLearning #DeepLearning #NeuralNetworks #Innovation #EvolutionaryAI #KVCache @sakanaai.bsky.social @arxiv-stat-ml.bsky.social