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Initiated and Officially Supported by Tensormesh
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Read more: LMCache supports gpt-oss (20B/120B) on Day 1
LMCache now supports OpenAI’s newly released GPT-OSS models (20B and 120B parameters) from day one! This post provides a complete guide to setting up vLLM with LMCache for GPT-OSS models and demonstrates significant performance improvements through our CPU offloading capabilities. Step 1: Installing vLLM GPT OSS Version Installation Test the Installation Step 2: Install LMCache…
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Read more: CacheGen: Store Your KV Cache on Disk or S3—Load Blazingly Fast!
TL;DR: 🚀 CacheGen lets you store KV caches on disk or AWS S3 and load them way faster than recomputing! It compresses your KV cache up to 3× smaller than quantization so that you can load your KV cache blazingly fast while keeping response quality high. Stop wasting compute — use CacheGen to fully utilize…
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Read more: Shortest Prefill First—Smarter Scheduling for Faster Prefill!
TL;DR: ⚡ Shortest Prefill First (SPF) scheduling cuts LLM time-to-first-token by up to 18% in prefill-decode disaggregation—unlocking even greater gains when combined with LMCache! At LMCache Lab, we’re obsessed with LLM performance. As prefill-decode disaggregation becomes the norm, we spotted a major, untapped scheduling opportunity for prefill nodes.That’s why we developed SPF (Shortest Prefill First,…
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Read more: LMCache Lab: Only prefilling? We reduce decoding latency by 60%!
TL;DR: 🚀 LMCache Lab cuts decoding latency for code/text editing by 60% with speculative decoding! ⚡ You might know LMCache Lab for our KV cache optimizations that make LLM prefilling a breeze. But that’s not all! We’re now focused on speeding up decoding too—so your LLM agents can generate new content even faster. In other…
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Read more: LMIgnite: Fastest LLM Inference for Conversational and Long-Document AI, Only One Click Away
TL;DR: LLMs are transforming every product and service—from chatbots and copilots to intelligent document search and enterprise workflows. But running LLMs in production is still painfully slow, prohibitively expensive, and complex to manage. That changes today. We’re excited to announce the launch of LMIgnite — the first one-click deployable high-performance LLM inference backend for Conversational…
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Read more: Speeding Up LLM Inference: Beyond the Inference Engine
TL;DR: LLMs are rapidly becoming the dominant workload in enterprise AI. As more applications rely on real-time generation, inference performance — measured in speed, cost, and reliability — becomes the key bottleneck. Today, the industry focuses primarily on speeding up inference engines like vLLM, SGLang, and TensorRT. But in doing so, we’re overlooking a much…
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Read more: LMCache Extends Its Turbo-Boost to Multimodal Models in vLLM V1
TL;DR: The latest LMCache release plugs seamlessly into vLLM’s new multimodal stack. By hashing image-side tokens (mm_hashes) and caching their key-value (KV) pairs, LMCache reuses vision embeddings across requests—slashing time-to-first-token and GPU memory for visual-LLMs. Summary — Why This Matters Multimodal large language models (MLLMs) multiply KV-cache traffic: every image can add thousands of “vision…
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Read more: LLM Production Stack Goes Cross-Hardware: Ascend, Arm, and AMD Support Incoming
TL;DR: Our LLM Production Stack project just hit another milestone. We’re integrating with more hardware accelerators — including Ascend, Arm, and AMD — signaling growing maturity and broader applicability across enterprise and research settings. 🚀 LMCache Is Gaining Traction LMCache has quietly become the unsung hero in the LLM inference world. As a core component…
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Read more: LMCache Announces Exciting Collaboration with Red Hat, with LMCache Serving as a Founding Supporter of the llm-d project
We’re delighted to announce that LMCache is joining forces with Red Hat and other industry leaders on some exciting open source project collaborations. LMCache has been selected to be a core component of llm-d, a new open source project led by Red Hat to drive more scalable, efficient distributed inferencing across clusters of vLLM servers…
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Read more: How LMCache Turbocharges Enterprise LLM Inference Frameworks
TL;DR LMCache, the state-of-the-art KV cache layer library developed by TensorMesh and the project’s open-source community, delivers breakthrough performance improvements to modern enterprise LLM inference frameworks, including the vLLM Production Stack, KServe, and NVIDIA’s Dynamo. With fast and scalable caching of long-context KV cache, LMCache helps reduce inference costs and ensures SLOs for both latency…
