LMCache vLLM

Related Products

  • LM-Kit.NET
    4.8 out of 5 stars 29 Ratings
    Visit Website for LM-Kit.NET
  • KrakenD
    4.7 out of 5 stars 76 Ratings
    Visit Website for KrakenD
  • Gemini platform
    4.3 out of 5 stars 1,178 Ratings
    Visit Website for Gemini platform
  • Compute with Hivenet
    5.0 out of 5 stars 4 Ratings
    Visit Website for Compute with Hivenet
  • SearchUnify
    4.5 out of 5 stars 150 Ratings
    Visit Website for SearchUnify
  • TelemetryOS
    4.8 out of 5 stars 282 Ratings
    Visit Website for TelemetryOS
  • Runpod
    4.2 out of 5 stars 230 Ratings
    Visit Website for Runpod
  • Couchbase
    4.4 out of 5 stars 418 Ratings
    Visit Website for Couchbase
  • IONOS Cloud GPU Servers
    4.6 out of 5 stars 45,816 Ratings
    Visit Website for IONOS Cloud GPU Servers
  • Sogolytics
    4.7 out of 5 stars 869 Ratings
    Visit Website for Sogolytics
LMCache:

About

LMCache is an open source Knowledge Delivery Network (KDN) designed as a caching layer for large language model serving that accelerates inference by reusing KV (key-value) caches across repeated or overlapping computations. It enables fast prompt caching, allowing LLMs to “prefill” recurring text only once and then reuse those stored KV caches, even in non-prefix positions, across multiple serving instances. This approach reduces time to first token, saves GPU cycles, and increases throughput in scenarios such as multi-round question answering or retrieval augmented generation. LMCache supports KV cache offloading (moving cache from GPU to CPU or disk), cache sharing across instances, and disaggregated prefill, which separates the prefill and decoding phases for resource efficiency. It is compatible with inference engines like vLLM and TGI and supports compressed storage, blending techniques to merge caches, and multiple backend storage options.
vLLM:

About

vLLM is a high-performance library designed to facilitate efficient inference and serving of Large Language Models (LLMs). Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. It offers state-of-the-art serving throughput by efficiently managing attention key and value memory through its PagedAttention mechanism. It supports continuous batching of incoming requests and utilizes optimized CUDA kernels, including integration with FlashAttention and FlashInfer, to enhance model execution speed. Additionally, vLLM provides quantization support for GPTQ, AWQ, INT4, INT8, and FP8, as well as speculative decoding capabilities. Users benefit from seamless integration with popular Hugging Face models, support for various decoding algorithms such as parallel sampling and beam search, and compatibility with NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs, and more.
LMCache:

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported
vLLM:

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported
LMCache:

Audience

AI engineers and infrastructure teams looking for a tool to lower latency, reduce compute cost, and scale throughput
vLLM:

Audience

AI infrastructure engineers looking for a solution to optimize the deployment and serving of large-scale language models in production environments
LMCache:

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported
vLLM:

Support

Phone Support Not Supported
24/7 Live Support Supported
Online Supported
LMCache:

API

Offers API Not Supported
vLLM:

API

Offers API Supported
LMCache:

Screenshots and Videos

vLLM:

Screenshots and Videos

LMCache:

Pricing

Free
Free Version Supported
Free Trial Not Supported
vLLM:

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported
LMCache:

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software
vLLM:

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software
LMCache:

Training

Documentation Supported
Webinars Not Supported
Live Online Supported
In Person Not Supported
vLLM:

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported
LMCache:

Company Information

LMCache
United States
lmcache.ai/
vLLM:

Company Information

vLLM
United States
vllm.ai
LMCache:

Alternatives

vLLM:

Alternatives

DeepInfer

DeepInfer

DeepSeek
LMCache:
BaseRT

BaseRT

Base Compute
vLLM:
LMCache:
DeepInfer

DeepInfer

DeepSeek
vLLM:
LMCache:
vLLM:
LMCache:
vLLM:
LMCache:

Categories

vLLM:

Categories

AI Inference Supported
LMCache:

Integrations

Compute with Hivenet Not Supported
DashboardGPT Not Supported
Database Mart Not Supported
Docker Not Supported
DocsGPT Not Supported
Hugging Face Not Supported
Kubernetes Not Supported
LFM3 Not Supported
ModelLane Not Supported
Muse Spark Open Not Supported
NVIDIA DRIVE Not Supported
Nemotron 3.5 Bolt Not Supported
Nemotron 3.5 Transcribe Not Supported
OpenAI Not Supported
OptScale AI Not Supported
PyTorch Not Supported
Recursive Flint Not Supported
Shift-Left API Not Supported
Thunder Compute Not Supported
omp Not Supported
vLLM:

Integrations

Compute with Hivenet Supported
DashboardGPT Supported
Database Mart Supported
Docker Supported
DocsGPT Supported
Hugging Face Supported
Kubernetes Supported
LFM3 Supported
ModelLane Supported
Muse Spark Open Supported
NVIDIA DRIVE Supported
Nemotron 3.5 Bolt Supported
Nemotron 3.5 Transcribe Supported
OpenAI Supported
OptScale AI Supported
PyTorch Supported
Recursive Flint Supported
Shift-Left API Supported
Thunder Compute Supported
omp Supported
LMCache:
Claim LMCache and update features and information
Claim LMCache and update features and information
vLLM:
Claim vLLM and update features and information
Claim vLLM and update features and information