Conversion GemsConversion Gems
Submit a tool
AI Tools · AI Development

Lmdeploy

LMDeploy is a free, open-source toolkit for compressing, deploying, and serving large language models at high throughput.

Our take: LMDeploy is a free, Apache 2.0-licensed LLM inference optimization library built by the InternLM/MMRazor team at Shanghai AI Lab.

Best forDevelopers
ReviewedPricing: Free
7.5 out of 105 criteria · see breakdown
Try LmdeployFree (Apache 2.0 open source)
The bottom line

A top-tier open-source LLM inference engine for ML teams that need maximum GPU throughput and quantization efficiency — at zero cost.

Our score

Reviewed by the Conversion Gems editorial team. Hover a facet to focus it; click to pin.Tap a facet to focus it.

What it is, and why we rate it

One line each. Open a row for detail.

What it really isLMDeploy — open-source toolkit for compressing, deploying, and serving large language models at high throughput.
Our takeLMDeploy is a free, Apache 2.0-licensed LLM inference optimization library built by the InternLM/MMRazor team at Shanghai AI Lab.
It is a low-level developer tool for running LLMs in production with maximum efficiency, benchmarking up to 1.8x the request throughput of vLLM. This is a fully open-source, zero-cost project.
Why we rate itLMDeploy consistently benchmarks among the top LLM inference runtimes for throughput and 4-bit quantization speed, supporting 60+ model families including Llama, Qwen, DeepSeek, and InternLM — all at zero cost.
The catchNo managed service, no GUI, and no commercial support — requires deep ML and CUDA infrastructure expertise to operate.
Community support via GitHub only.

Best / not good for

Best for
  • ML engineering teams self-hosting LLMs on GPU clusters
  • AI infrastructure teams optimizing throughput and GPU utilization
  • Researchers needing fast 4-bit quantized inference
Not good for
  • Teams wanting a managed LLM API with no infrastructure overhead
  • Non-technical product teams or business users
  • Organizations requiring enterprise SLAs or dedicated vendor support
Trade-offs

Friction report

Trade-offs to know before signing up.

  1. Time to value
    Slow — requires Python/CUDA environment setup, model download, quantization configuration, and tuning before production serving.
  2. Scale breakpoint
    Multi-GPU and multi-node distributed setups require manual distributed configuration; no built-in auto-scaling.
  3. Walled garden
    Low — fully open source under Apache 2.0; no vendor lock-in, freely portable across GPU vendors.
Still a fit?
If none of these block you, start on the free plan.
Try Lmdeploy

Frequently Asked Questions

How does Lmdeploy assist with deploying AI or ML models?

Lmdeploy assists with deploying AI or ML models by providing automated serving, scaling, and monitoring tools.

How does Lmdeploy support AI model deployment?

Lmdeploy supports AI model deployment by packaging, serving, and scaling models for production use.

How does Lmdeploy assist in deploying AI models efficiently?

Lmdeploy assists in deploying AI models efficiently with automation, monitoring, and integration tools.

What features make Lmdeploy helpful for deploying AI models?

Lmdeploy is helpful for deploying AI models with infrastructure, scaling, and API integration tools.

How can Lmdeploy assist in deploying AI and language models?

Lmdeploy assists in deploying AI and language models with scalable hosting, monitoring, and API endpoints.

Alternatives

  • Step up

    vLLM for a more widely adopted inference runtime with a larger community and managed cloud options.

  • Lighter alternative

    Ollama for a simpler, desktop-friendly local LLM runner requiring minimal infrastructure expertise.

Showing 1–2 of 2

Conversion Gems independently reviews every tool.

Tags

#LocalLLM#LLMTools#OpenSourceAI

Explore related categories

Compare AI Development softwareAI Development