Comparison
flashinfer vs sarathi-serve
Verdict
Pick flashinfer if flashInfer is a Python library that optimizes inference for large-scale language models through the application of CUDA and GPU support; pick sarathi-serve if sarathi Serve targets efficient low-latency and high-throughput inference for LLMs using Python.
Markdown twin · flashinfer alternatives · sarathi-serve alternatives
GraphCanon updated Sep 20, 2026
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Trust & integrity
| Signal | flashinfer | sarathi-serve |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 19, 2026 · github_public_v1 | Slowing (254d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 19, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- flashinfer
- FlashInfer is a kernel library for serving large language models
- sarathi-serve
- A low-latency and high-throughput serving engine for LLMs
Stars
- flashinfer
- 6.5k
- sarathi-serve
- 527
Forks
- flashinfer
- 1.5k
- sarathi-serve
- 67
Open issues
- flashinfer
- 953
- sarathi-serve
- 17
Language
- flashinfer
- Cuda
- sarathi-serve
- Python
Adopt for
- flashinfer
- FlashInfer is a Python library that optimizes inference for large-scale language models through the application of CUDA and GPU support.
- sarathi-serve
- Sarathi Serve targets efficient low-latency and high-throughput inference for LLMs using Python.
Persona
- flashinfer
- -
- sarathi-serve
- -
Runtime
- flashinfer
- -
- sarathi-serve
- -
License
- flashinfer
- Apache-2.0
- sarathi-serve
- Apache-2.0
Last pushed
- flashinfer
- Sep 19, 2026
- sarathi-serve
- Jan 8, 2026
Categories
- flashinfer
- Inference & Serving, LLM Frameworks
- sarathi-serve
- Inference & Serving
Trust and health
Maintenance
- flashinfer
- Very active (96%)
- sarathi-serve
- Slowing (36%)
Days since push
- flashinfer
- 0d
- sarathi-serve
- 254d
Open issues (now)
- flashinfer
- 953
- sarathi-serve
- 17
Stars delta
- flashinfer
- +428 (30d)
- sarathi-serve
- +15 (30d)
Open issues delta
- flashinfer
- +124 (30d)
- sarathi-serve
- +1 (30d)
Full report
- flashinfer
- Trust report
- sarathi-serve
- Trust report
Shared compatibility
- Python · flashinfer: Python runtime · sarathi-serve: Python runtime
Choose flashinfer if…
- flashinfer is primarily Cuda; sarathi-serve is Python.
- Tags unique to flashinfer: attention, cuda, distributed-inference, gpu.
- Also covers LLM Frameworks.
- When aiming to deploy large language models efficiently using CUDA capabilities, maximizing GPU utilization with FlashInfer can be advantageous.
When NOT to use flashinfer
- If the project does not involve large-scale language models or has limited GPU resources, FlashInfer’s specialized features may offer fewer benefits.
- For those preferring frameworks integrated closely with other deep learning APIs beyond PyTorch, considering alternatives might better align with diverse tooling requirements.
Choose sarathi-serve if…
- sarathi-serve is primarily Python; flashinfer is Cuda.
- Tags unique to sarathi-serve: llama, pytorch, transformer.
- Optimize Python-based projects needing quick responses from large language models.
When NOT to use sarathi-serve
- Necessitate a non-Python environment for deployment and operation.
- Prefer a tool that incorporates more than just low-latency, high-throughput focus such as multi-language support or specialized optimizations.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (flashinfer-ai/flashinfer) · observed Sep 20, 2026
- GitHub forks (flashinfer-ai/flashinfer) · observed Sep 20, 2026
- Last push (flashinfer-ai/flashinfer) · observed Sep 19, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (microsoft/sarathi-serve) · observed Sep 20, 2026
- GitHub forks (microsoft/sarathi-serve) · observed Sep 20, 2026
- Last push (microsoft/sarathi-serve) · observed Jan 8, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: flashinfer 6.5k · sarathi-serve 527 (synced Sep 20, 2026).
Common questions
- What is the difference between flashinfer and sarathi-serve?
- flashinfer: FlashInfer is a kernel library for serving large language models. sarathi-serve: A low-latency and high-throughput serving engine for LLMs. See the comparison table for live GitHub stats and shared categories.
- When should I choose flashinfer over sarathi-serve?
- Choose flashinfer over sarathi-serve when flashinfer is primarily Cuda; sarathi-serve is Python; Tags unique to flashinfer: attention, cuda, distributed-inference, gpu; Also covers LLM Frameworks; When aiming to deploy large language models efficiently using CUDA capabilities, maximizing GPU utilization with FlashInfer can be advantageous.
- When should I choose sarathi-serve over flashinfer?
- Choose sarathi-serve over flashinfer when sarathi-serve is primarily Python; flashinfer is Cuda; Tags unique to sarathi-serve: llama, pytorch, transformer; Optimize Python-based projects needing quick responses from large language models.
- When should I avoid flashinfer?
- If the project does not involve large-scale language models or has limited GPU resources, FlashInfer’s specialized features may offer fewer benefits. For those preferring frameworks integrated closely with other deep learning APIs beyond PyTorch, considering alternatives might better align with diverse tooling requirements.
- When should I avoid sarathi-serve?
- Necessitate a non-Python environment for deployment and operation. Prefer a tool that incorporates more than just low-latency, high-throughput focus such as multi-language support or specialized optimizations.
- Is flashinfer or sarathi-serve more popular on GitHub?
- flashinfer has more GitHub stars (6,452 vs 527). Stars measure visibility, not whether either tool fits your constraints.
- Are flashinfer and sarathi-serve open source?
- Yes - both are open-source projects on GitHub (flashinfer: Apache-2.0, sarathi-serve: Apache-2.0).
- Where can I find alternatives to flashinfer or sarathi-serve?
- GraphCanon lists graph-backed alternatives at flashinfer alternatives and sarathi-serve alternatives (flashinfer markdown twin, sarathi-serve 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, flashinfer or sarathi-serve?
- flashinfer: Very active. sarathi-serve: Slowing. 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 flashinfer and sarathi-serve?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: flashinfer trust report; sarathi-serve trust report.