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About

Tensormesh is a caching layer built specifically for large-language-model inference workloads that enables organizations to reuse intermediate computations, drastically reduce GPU usage, and accelerate time-to-first-token and latency. It works by capturing and reusing key-value cache states that are normally thrown away after each inference, thereby cutting redundant compute and delivering “up to 10x faster inference” while substantially lowering GPU load. It supports deployments in public cloud or on-premises, with full observability and enterprise-grade control, SDKs/APIs, and dashboards for integration into existing inference pipelines, and compatibility with inference engines such as vLLM out of the box. Tensormesh emphasizes performance at scale, including sub-millisecond repeated queries, while optimizing every layer of inference from caching through computation.

About

oMLX is a macOS-native MLX server designed to make local AI faster and more practical on Apple Silicon. Built for the way coding agents actually work, it uses paged SSD KV caching to persist cache blocks to disk, allowing previously seen prefixes to be restored across requests and server restarts instead of being recomputed from scratch. This can reduce time to first token on long contexts from 30–90 seconds to under five seconds after the first turn. Continuous batching handles concurrent requests through mlx-lm’s BatchGenerator, improving generation throughput without forcing requests to wait behind a single job. oMLX can serve LLMs, vision-language models, embedding models, and rerankers simultaneously, using LRU eviction when memory runs low. It supports any MLX-format model from Hugging Face, including Qwen, LLaMA, Mistral, Gemma, DeepSeek, MiniMax, and GLM, and can reuse models already stored in the standard Hugging Face cache, LM Studio folders, or custom directories.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Enterprises and AI infrastructure teams wanting a tool to reduce latency and cost while maintaining full control over deployment and data

Audience

Developers and AI power users needing to run fast local LLM inference and agentic coding workflows on Apple Silicon

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

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

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

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Tensormesh
Founded: 2025
United States
www.tensormesh.ai/

Company Information

oMLX
United States
omlx.ai/

Alternatives

Alternatives

Photon

Photon

Moondream
Run BiOS

Run BiOS

UltraSafe AI Inc.
BaseRT

BaseRT

Base Compute
Photon

Photon

Moondream
Macyou

Macyou

Macyou LLC

Categories

Categories

Integrations

Anthropic
Claude Code
Cursor
DeepSeek
GLM-4.1V
Gemma
Gemma
GitHub
Hugging Face
JSON
LM Studio
Llama
MiniMax
Mistral AI
Model Context Protocol (MCP)
OpenAI
OpenClaw
Python
Qwen

Integrations

Anthropic
Claude Code
Cursor
DeepSeek
GLM-4.1V
Gemma
Gemma
GitHub
Hugging Face
JSON
LM Studio
Llama
MiniMax
Mistral AI
Model Context Protocol (MCP)
OpenAI
OpenClaw
Python
Qwen
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Claim Tensormesh and update features and information
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