LMDeploy

At a glance

• Editor score
  7.4 / 10
• Pricing
  Free plan
• Best for
  Quantized LLM/VLM server deployments
• Free plan
  Yes
• Paid from
  Free
• GPU acceleration
  Yes
• Facts checked
  13 Oct 2026

• Where it wins

  • TurboMind C++/CUDA and Python PyTorch inference engines
  • OpenAI-compatible serving with batching and tensor parallelism
  • AWQ, SmoothQuant, KV-cache, and S-LoRA support

• Where it doesn't

  • Self-hosted operations require your team to manage infrastructure
  • Community and documentation are the listed support channels
  • Focused on inference and serving rather than broader training workflows

Our verdict on LMDeploy

LMDeploy is an open-source Python toolkit for compressing, deploying, and serving large language models and vision-language models. It suits teams that need to run quantized models on their own infrastructure, from smaller deployments to enterprise-scale multi-machine serving. The project is distributed under the Apache 2.0 license and supports Linux, Windows, and API-based access, with deployment targets spanning servers and edge environments.

Its engine choice is a practical differentiator. TurboMind provides a C++/CUDA path, while the Python PyTorch engine supports a more Python-oriented workflow. Offline LLM and VLM pipelines cover batch-style inference, and OpenAI-compatible API serving supports application integration. Continuous batching, tensor parallelism, multi-model serving, and multi-machine deployment address workloads that need concurrent requests or distributed execution. Documentation covers CUDA, ROCm, and other accelerator backends, giving platform teams several implementation paths.

Quantization is central to the product: published model formats include FP16, BF16, W4A16, W8A8, KV INT8, and KV INT4. AWQ, SmoothQuant, and KV-cache quantization provide distinct ways to reduce inference cost or memory demand, while S-LoRA adapter serving helps teams host adapter-based variants. LMDeploy is a strong fit when you want control over model compression, runtime selection, and self-managed serving. Choose another option if you need vendor-managed hosting, dedicated support, or a platform centered on model training rather than inference operations.

Your product? Our editors can test LMDeploy in depth and publish a review, labelled as paid · from $149/mo →

LMDeploy pricing

LMDeploy fact sheet

Alternatives to LMDeploy

• NTNVIDIA TensorRTA free SDK for compiling and running optimized deep-learning inference on NVIDIA GPUs.9.4
• vLLMA flexible open-source engine for teams serving LLMs across varied infrastructure.9.3
• ATApache TVMA flexible open-source compiler for deploying models across CPUs, GPUs, mobile and edge hardware.9.2

See all LMDeploy alternatives →

Featured on iTechGuides

LMDeploy is listed in our AI Inference Engines directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 29 Oct 2026 · How we research and update