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Initiated and Officially Supported by Tensormesh
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Read more: LMCache Multi-node P2P CPU Memory Sharing & Control: From Experimental Feature to Production
Baolong Mao (Tencent), Chunxiao Zheng (Tencent), Weishu Deng (Tensormesh), Darren Peng (Tensormesh), Samuel Shen (Tensormesh) What is P2P and what does it promise? In this blog post, we will go over: Most production vLLM deployments run multiple identical instances behind a load balancer. Each instance builds its own KV cache only from the traffic it…
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Read more: AMD × LMcache: AMD GPU Acceleration with LMcache
Introduction LLM inference becomes increasingly challenging as context length grows and workloads scale. Traditional serving engines rely on prefix-based KV cache reuse, which limits opportunities for optimization, especially when processing long, repeated, or overlapping text across different requests. LMCache addresses this challenge. It is an extension to LLM serving engines that dramatically reduces time-to-first-token (TTFT)…
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Read more: Context Engineering & Reuse Pattern Under the Hood of Claude Code
Over the last few months, Claude Code has quietly become one of the most interesting & widely-adopted real-world agentic systems available to normal developers. Unlike cloud-only agents whose internals remain hidden behind API gateways like Perplexity, Devin, or Manus, nor as fully open source agents like Mini SWE Agent or Terminus 2 where you can…
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Read more: LMCache x Ascend: Accelerating LLM inference on Ascend NPUs
Supporting Ascend NPUs We’re delighted to announce that LMCache now officially supports Ascend NPUs with the release of the LMCache-Ascend plugin. LMCache-Ascend supports a broad range of Ascend compute platforms from the cloud to the edge. This major platform expansion underscores LMCache’s commitment to delivering leading performance across a diverse hardware ecosystem, enabling developers to…
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Read more: Tensormesh unveiled and LMCache joins the PyTorch Foundation
Announcing Tensormesh First I wanted to repeat here what I posted on the LMCache #general Slack channel last week: I am delighted to announce that the team that founded the LMCache project has decided to form a company, Tensormesh, a few months ago. As we are announcing the beta of our first product, we have…
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Read more: Breaking the Memory Barrier: How LMCache and CoreWeave Power Efficient LLM Inference for Cohere
The challenge: Scaling enterprise AI Enterprises today are racing to integrate large language models (LLMs) into their products and workflows, but doing it at scale brings challenges in performance, cost, and accuracy. Organizations need models to be based on their specific data, while making sure that this information remains private. Cohere, one of the leading…
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Read more: LMCache on Google Kubernetes Engine: Boosting LLM Inference Performance with KV Cache on Tiered Storage
Overview of the Collaboration The KV Cache is a memory optimization that makes Large Language Models(LLMs) run the forward pass faster by storing Key (K) and Value (V) matrices to prevent the model from recalculating them for the entire text sequence with every new generated token. Maximizing the KV Cache hit rate with storage is…
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Read more: Implementing LMCache Plugin Framework & lmcache_frontend: Design Philosophy
A flexible plugin system for enhanced observability and management Abstract In large-scale language model inference scenarios, efficient memory management and KV cache optimization are crucial. LMCache, as a KV cache management system specifically designed for vLLM, requires more flexible extension mechanisms to meet the needs of monitoring, troubleshooting, and state insight when facing complex production…
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Read more: NVIDIA Dynamo integrates LMCache, Accelerating LLM Inference
We’re thrilled to announce that Nvidia Dynamo has integrated LMCache as a KV caching layer solution. This is a big milestone: Dynamo gets a battle-tested caching solution, and LMCache becomes part of a data center-scale inference platform used by many developers worldwide to deploy AI at scale. For comprehensive details about Dynamo’s KV cache optimization…
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Read more: Extending LMCache Backends: A Comprehensive Guide to Custom Backend Development
In large language model inference scenarios, the performance and flexibility of KVCache caching systems directly impact overall service efficiency. LMCache, as a high-performance large model caching framework, provides developers with rich extension capabilities through its modular backend design. This article will start with LMCache backend’s extension mechanism, using the officially provided lmc_external_log_backend as an example,…
