Comparison
litgpt vs vllm-mlx
Verdict
Pick litgpt if litGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment; pick vllm-mlx if vllm-mlx is an open-source inference server that runs large language models and vision-language models on Apple Silicon devices with continuous batching and multimodal support using native MLX backend.
Markdown twin · litgpt alternatives · vllm-mlx alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | litgpt | vllm-mlx |
|---|---|---|
| Maintenance | Active (17d since push) As of 2w · github_public_v1 | Steady (31d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- litgpt
- High-performance LLMs with recipes for pretraining, finetuning and deployment
- vllm-mlx
- Server for LLMs and vision-language models compatible with Apple Silicon
Stars
- litgpt
- 14k
- vllm-mlx
- 1.5k
Forks
- litgpt
- 1.5k
- vllm-mlx
- 205
Open issues
- litgpt
- 272
- vllm-mlx
- 86
Language
- litgpt
- Python
- vllm-mlx
- Python
Adopt for
- litgpt
- LitGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment.
- vllm-mlx
- vllm-mlx is an open-source inference server that runs large language models and vision-language models on Apple Silicon devices with continuous batching and multimodal support using native MLX backend.
Persona
- litgpt
- -
- vllm-mlx
- -
Runtime
- litgpt
- -
- vllm-mlx
- -
License
- litgpt
- LitGPT operates under the open-source Apache-2.0 license, providing permissive terms for use and modification.
- vllm-mlx
- Apache-2.0
Last pushed
- litgpt
- Jul 20, 2026
- vllm-mlx
- Jun 28, 2026
Categories
- litgpt
- Inference & Serving, LLM Frameworks, Model Training
- vllm-mlx
- Inference & Serving, Model Training
Trust and health
Maintenance
- litgpt
- Active (82%)
- vllm-mlx
- Steady (60%)
Days since push
- litgpt
- 17d
- vllm-mlx
- 31d
Open issues (now)
- litgpt
- 272
- vllm-mlx
- 86
Stars delta
- litgpt
- +137 (30d)
- vllm-mlx
- Unknown
Open issues delta
- litgpt
- +6 (30d)
- vllm-mlx
- Unknown
Owner type
- litgpt
- Organization
- vllm-mlx
- User
Full report
- litgpt
- Trust report
- vllm-mlx
- Trust report
Shared compatibility
- Python · litgpt: Python runtime · vllm-mlx: Python runtime
Choose litgpt if…
- Pricing: The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models..
- Requirements: Min 16 GB RAM.
- Tags unique to litgpt: ai, artificial-intelligence, deep-learning, large language models.
- Also covers LLM Frameworks.
- If you are focusing on a project that requires rapid prototyping or experimentation with over 20 different LLMs to find the best fit for your application.
When NOT to use litgpt
- If you need a tool specifically optimized for resource-constrained devices, as LitGPT focuses on high-performance LLMs and may require more resources.
- When your project is strictly limited to only one or two types of specific LLMs; in this case, another specialized framework that caters narrowly might be preferable.
Choose vllm-mlx if…
- Tags unique to vllm-mlx: anthropic, apple-silicon, audio-processing, claude-code.
- If you need to run LLMs or vision-language models like Llama, Qwen-VL, and LLaVA efficiently on Apple Silicon devices.
- Leaner open-issue backlog (86).
When NOT to use vllm-mlx
- If your target environment is not an Apple device equipped with the required hardware to run models via MLX backend.
- When seeking a solution that offers high-speed token throughput beyond 400 tok/s as vllm-mlx may not be adequate for such performance needs.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Lightning-AI/litgpt) · observed Aug 7, 2026
- GitHub forks (Lightning-AI/litgpt) · observed Aug 7, 2026
- Last push (Lightning-AI/litgpt) · observed Jul 20, 2026
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (waybarrios/vllm-mlx) · observed Jul 30, 2026
- GitHub forks (waybarrios/vllm-mlx) · observed Jul 30, 2026
- Last push (waybarrios/vllm-mlx) · observed Jun 28, 2026
- License file (Apache-2.0) · observed Jul 30, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: litgpt 14k · vllm-mlx 1.5k (synced Aug 7, 2026).
Common questions
- What is the difference between litgpt and vllm-mlx?
- litgpt: High-performance LLMs with recipes for pretraining, finetuning and deployment. vllm-mlx: Server for LLMs and vision-language models compatible with Apple Silicon. See the comparison table for live GitHub stats and shared categories.
- When should I choose litgpt over vllm-mlx?
- Choose litgpt over vllm-mlx when Pricing: The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models.; Requirements: Min 16 GB RAM; Tags unique to litgpt: ai, artificial-intelligence, deep-learning, large language models; Also covers LLM Frameworks; If you are focusing on a project that requires rapid prototyping or experimentation with over 20 different LLMs to find the best fit for your application.
- When should I choose vllm-mlx over litgpt?
- Choose vllm-mlx over litgpt when Tags unique to vllm-mlx: anthropic, apple-silicon, audio-processing, claude-code; If you need to run LLMs or vision-language models like Llama, Qwen-VL, and LLaVA efficiently on Apple Silicon devices; Leaner open-issue backlog (86).
- When should I avoid litgpt?
- If you need a tool specifically optimized for resource-constrained devices, as LitGPT focuses on high-performance LLMs and may require more resources. When your project is strictly limited to only one or two types of specific LLMs; in this case, another specialized framework that caters narrowly might be preferable.
- When should I avoid vllm-mlx?
- If your target environment is not an Apple device equipped with the required hardware to run models via MLX backend. When seeking a solution that offers high-speed token throughput beyond 400 tok/s as vllm-mlx may not be adequate for such performance needs.
- Is litgpt or vllm-mlx more popular on GitHub?
- litgpt has more GitHub stars (13,605 vs 1,472). Stars measure visibility, not whether either tool fits your constraints.
- Are litgpt and vllm-mlx open source?
- Yes - both are open-source projects on GitHub (litgpt: Apache-2.0, vllm-mlx: Apache-2.0).
- Where can I find alternatives to litgpt or vllm-mlx?
- GraphCanon lists graph-backed alternatives at litgpt alternatives and vllm-mlx alternatives (litgpt markdown twin, vllm-mlx markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, litgpt or vllm-mlx?
- litgpt: Active. vllm-mlx: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for litgpt and vllm-mlx?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: litgpt trust report; vllm-mlx trust report.