Comparison
JetStream vs vllm
Verdict
Pick JetStream if jetStream optimises throughput and memory for LLM inference on XLA devices like TPUs, with potential GPU support in future; pick vllm if vLLM is a specialized inference engine for large language models that prioritizes high throughput and memory efficiency, suitable for deployment across different hardware backends.
Markdown twin · JetStream alternatives · vllm alternatives
GraphCanon updated 3w
Trust & integrity
| Signal | JetStream | vllm |
|---|---|---|
| Maintenance | Slowing (201d since push) As of 1mo · github_public_v1 | Very active (0d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1mo · github_public_v1 | Not a fork · Organization 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
- JetStream
- Throughput and memory optimized engine for LLM inference on XLA devices
- vllm
- A high-throughput and memory-efficient inference and serving engine for LLMs
Stars
- JetStream
- 451
- vllm
- 88k
Forks
- JetStream
- 67
- vllm
- 20k
Open issues
- JetStream
- 25
- vllm
- 6.2k
Language
- JetStream
- Python
- vllm
- Python
Adopt for
- JetStream
- JetStream optimises throughput and memory for LLM inference on XLA devices like TPUs, with potential GPU support in future.
- vllm
- vLLM is a specialized inference engine for large language models that prioritizes high throughput and memory efficiency, suitable for deployment across different hardware backends.
Persona
- JetStream
- -
- vllm
- -
Runtime
- JetStream
- -
- vllm
- -
License
- JetStream
- Apache-2.0
- vllm
- Apache-2.0
Last pushed
- JetStream
- Jan 5, 2026
- vllm
- Aug 1, 2026
Categories
- JetStream
- Inference & Serving
- vllm
- Inference & Serving
Trust and health
Maintenance
- JetStream
- Slowing (36%)
- vllm
- Very active (96%)
Days since push
- JetStream
- 201d
- vllm
- 0d
Open issues (now)
- JetStream
- 25
- vllm
- 6.2k
Full report
- JetStream
- Trust report
- vllm
- Trust report
Shared compatibility
- Python · JetStream: Python runtime · vllm: Python runtime
Choose JetStream if…
- Tags unique to JetStream: gemma, gpu, jax, large language models.
- * You are working with large language models (LLMs) that require efficient inference on hardware supported by XLA, particularly TPUs.
- Leaner open-issue backlog (25).
When NOT to use JetStream
- * If your primary compute platform is not an XLA-compatible device such as TPU; JetStream's current focus is on systems that are supported by XLA.
- * When you need immediate support for GPUs, since GPU functionality is marked as a future potential enhancement.
Choose vllm if…
- Pricing: vLLM operates under the Apache-2.0 license, so it's entirely free to use without direct monetary costs, but users might incur costs related to hardware and cloud services required for deployment..
- Requirements: Installation can be done via `uv pip install vllm` or by building from source, allowing flexibility in how the tool is set up..
- Tags unique to vllm: amd, cuda, deepseek, llm-serving.
- When you need to deploy large language models with requirements for both high throughput and low resource consumption.
When NOT to use vllm
- Avoid using vLLM if your application strictly limits itself to a single type of hardware without needing cross-platform compatibility, as it may introduce unnecessary complexity.
- If memory efficiency is not a concern and you are optimizing for simplicity over resource management, alternatives with less configuration might be preferable.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (AI-Hypercomputer/JetStream) · observed Jul 26, 2026
- GitHub forks (AI-Hypercomputer/JetStream) · observed Jul 26, 2026
- Last push (AI-Hypercomputer/JetStream) · observed Jan 5, 2026
- License file (Apache-2.0) · observed Jul 26, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (vllm-project/vllm) · observed Aug 1, 2026
- GitHub forks (vllm-project/vllm) · observed Aug 1, 2026
- Last push (vllm-project/vllm) · observed Aug 1, 2026
- License file (Apache-2.0) · observed Aug 1, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: JetStream 451 · vllm 88k (synced Jul 26, 2026).
Common questions
- What is the difference between JetStream and vllm?
- JetStream: Throughput and memory optimized engine for LLM inference on XLA devices. vllm: A high-throughput and memory-efficient inference and serving engine for LLMs. See the comparison table for live GitHub stats and shared categories.
- When should I choose JetStream over vllm?
- Choose JetStream over vllm when Tags unique to JetStream: gemma, gpu, jax, large language models; * You are working with large language models (LLMs) that require efficient inference on hardware supported by XLA, particularly TPUs; Leaner open-issue backlog (25).
- When should I choose vllm over JetStream?
- Choose vllm over JetStream when Pricing: vLLM operates under the Apache-2.0 license, so it's entirely free to use without direct monetary costs, but users might incur costs related to hardware and cloud services required for deployment.; Requirements: Installation can be done via
uv pip install vllmor by building from source, allowing flexibility in how the tool is set up.; Tags unique to vllm: amd, cuda, deepseek, llm-serving; When you need to deploy large language models with requirements for both high throughput and low resource consumption. - When should I avoid JetStream?
- * If your primary compute platform is not an XLA-compatible device such as TPU; JetStream's current focus is on systems that are supported by XLA. * When you need immediate support for GPUs, since GPU functionality is marked as a future potential enhancement.
- When should I avoid vllm?
- Avoid using vLLM if your application strictly limits itself to a single type of hardware without needing cross-platform compatibility, as it may introduce unnecessary complexity. If memory efficiency is not a concern and you are optimizing for simplicity over resource management, alternatives with less configuration might be preferable.
- Is JetStream or vllm more popular on GitHub?
- vllm has more GitHub stars (87,847 vs 451). Stars measure visibility, not whether either tool fits your constraints.
- Are JetStream and vllm open source?
- Yes - both are open-source projects on GitHub (JetStream: Apache-2.0, vllm: Apache-2.0).
- Where can I find alternatives to JetStream or vllm?
- GraphCanon lists graph-backed alternatives at JetStream alternatives and vllm alternatives (JetStream markdown twin, vllm 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, JetStream or vllm?
- JetStream: Slowing. vllm: Very active. 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 JetStream and vllm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: JetStream trust report; vllm trust report.