LMDeploy
A framework for optimizing, deploying, and serving large language models
- Also known as
- lmdeploy
What is LMDeploy?
LMDeploy is a comprehensive toolkit enabling efficient compression, deployment, and serving of large language models across multiple machines and GPUs with advanced optimization techniques.
LMDeploy pricing
We don't have LMDeploy's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
What LMDeploy does
The capabilities that matter for model serving, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Continuous batching
- ✓
- Dynamic batching
- -
- Multi framework support
- -
- GPU acceleration
- ✓
- Multi GPU multi node
- ✓
- Quantization support
- ✓
- Openai compatible API
- -
- Autoscaling scale to zero
- -
- Multi model serving
- ✓
- Canary ab rollout
- -
- Kubernetes native
- -
- Open source
- -
Platform & deployment
Independently observed- CLI
- Windows
- Self-hosted
Integrations (2)
Independently observed- llm-compressor
- OpenCompass
Security & compliance
Known vulnerabilities: 10 (8 in the last 12 months), max severity CRITICAL sourcea count reflects scale & disclosure, not quality
LMDeploy FAQ
Common questions about LMDeploy, answered from independent, dated evidence.
What is LMDeploy?
A software framework that enables developers to compress, deploy, and serve large language models with quantization optimization, multiple inference engines, and compatibility across various model architectures. It is indexed under Model Serving.
Is LMDeploy free to use?
LMDeploy is open source, so it can be self-hosted and used at no licence cost. It is released under the Apache-2.0 licence. Pricing changes often, so verify at source before relying on it.
What platforms does LMDeploy support?
LMDeploy supports Windows and a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.
Can LMDeploy be self-hosted?
Yes. LMDeploy can be deployed self-hosted, so it does not have to run on the vendor's infrastructure.
Is LMDeploy open source?
Yes. LMDeploy is published under the Apache-2.0 licence, a permissive licence that generally allows commercial use and modification. Licence terms can change between releases, so verify against the repository for the version you intend to use.
LMDeploy alternatives
Other model serving we track, ranked by the same independent score.
- KServeA Kubernetes-based platform for deploying and scaling AI models across multiple frameworkshigh · 79%
- TorchServeA tool for hosting multiple PyTorch models in production with built-in logging and metricshigh · 76%
- Amazon SageMaker InferenceDeploy and serve machine learning models at scale with support for multiple frameworks and inference patternshigh · 75%
- NVIDIA Triton Inference Serverhigh · 77%
- SGLangA high-performance framework for efficiently serving language and multimodal models at scalelow · 10%
- NVIDIA NIMContainerized GPU-accelerated inference services for deploying AI models across cloud, on-premises, and edge environments.medium · 74%
Compare LMDeploy
Side by side against other model serving, attribute by attribute, with a source on every value.
The vioscaleAI score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for LMDeploy, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Development activity | 58 | 0.14 | 7.8 | ✓ |
| Release cadence | 88 | 0.08 | 7.2 | ✓ |
| Capabilities | 75 | 0.08 | 6.0 | ✓ |
| Stars | 74 | 0.05 | 3.5 | ✓ |
| Integrations | 14 | 0.05 | 0.7 | ✓ |
| Dependent projects | 8 | 0.05 | 0.4 | ✓ |
| Security posture | 0 | 0.12 | 0.0 | - |
| Package downloads | 0 | 0.04 | 0.0 | - |
| Security score | 0 | 0.05 | 0.0 | - |
| Developer Q&A activity | 0 | 0.14 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Activity
| Attribute | Value | Evidence |
|---|---|---|
| Commits last 30d | 66 | mediumsource · 2026-08-26 · 65% |
Adoption
Content
| Attribute | Value | Evidence |
|---|---|---|
| Faq | 5 items | mediumsource · 2026-09-10 · 67% |
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Gpu acceleration, Continuous batching, Multi model serving, Multi gpu multi node, Quantization support | mediumsource · 2026-10-13 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 2 | mediumsource · 2026-08-20 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-10-13 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-10-13 · 95% |
Pricing
Release
Security
| Attribute | Value | Evidence |
|---|---|---|
| Vulnerabilities | Count: 10 · Source: https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=pypi&package_name=lmdeploy&per_page=100 · Last 12m: 8 · Max severity: CRITICAL | highsource · 2026-10-13 · 90% |
Is LMDeploy the right choice for you?
Tell us the job, the constraints and what you weigh most, and we will rank LMDeploy against the rest of the model serving we index, using the same dated evidence weighted your way.
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