Comparison
REST vs flashinfer
Verdict
Pick REST if rEST is a retrieval-based speculative decoding method for enhancing the efficiency of large language model inference, implemented in C under the Apache-2.0 license; pick flashinfer if flashInfer is a Python library that optimizes inference for large-scale language models through the application of CUDA and GPU support.
Markdown twin · REST alternatives · flashinfer alternatives
GraphCanon updated Sep 20, 2026
12views this month
vs
Trust & integrity
| Signal | REST | flashinfer |
|---|---|---|
| Maintenance | Slowing (196d since push) As of Sep 18, 2026 · github_public_v1 | Very active (0d since push) As of Sep 19, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 19, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Sep 18, 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
- REST
- REST: Retrieval-Based Speculative Decoding
- flashinfer
- FlashInfer is a kernel library for serving large language models
Stars
- REST
- 220
- flashinfer
- 6.5k
Forks
- REST
- 17
- flashinfer
- 1.5k
Open issues
- REST
- 15
- flashinfer
- 953
Language
- REST
- C
- flashinfer
- Cuda
Adopt for
- REST
- REST is a retrieval-based speculative decoding method for enhancing the efficiency of large language model inference, implemented in C under the Apache-2.0 license.
- flashinfer
- FlashInfer is a Python library that optimizes inference for large-scale language models through the application of CUDA and GPU support.
Persona
- REST
- -
- flashinfer
- -
Runtime
- REST
- -
- flashinfer
- -
License
- REST
- Apache-2.0
- flashinfer
- Apache-2.0
Last pushed
- REST
- Mar 5, 2026
- flashinfer
- Sep 19, 2026
Categories
- REST
- Inference & Serving
- flashinfer
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- REST
- Slowing (36%)
- flashinfer
- Very active (96%)
Days since push
- REST
- 196d
- flashinfer
- 0d
Open issues (now)
- REST
- 15
- flashinfer
- 953
Stars delta
- REST
- 0 (30d)
- flashinfer
- +428 (30d)
Open issues delta
- REST
- 0 (30d)
- flashinfer
- +124 (30d)
OSV dependency advisories
- REST
- Published findings
- flashinfer
- No lockfile (source not queried)
Full report
- REST
- Trust report
- flashinfer
- Trust report
Shared compatibility
- Python · REST: Python runtime · flashinfer: Python runtime
Choose REST if…
- REST is primarily C; flashinfer is Cuda.
- Tags unique to REST: retrieval, speculative-decoding.
- Use REST when you need to improve the efficiency of large language model inference through retrieval-based speculative decoding.
When NOT to use REST
- Avoid REST if your project does not require improvements in the efficiency of large language model inference.
- Do not use REST if your project is not compatible with the C language or if you prefer a different license than Apache-2.0.
Choose flashinfer if…
- flashinfer is primarily Cuda; REST is C.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (FasterDecoding/REST) · observed Sep 20, 2026
- GitHub forks (FasterDecoding/REST) · observed Sep 20, 2026
- Last push (FasterDecoding/REST) · observed Mar 5, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- 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 on cards: REST 220 · flashinfer 6.5k (synced Sep 20, 2026).
Common questions
- What is the difference between REST and flashinfer?
- REST: REST: Retrieval-Based Speculative Decoding. flashinfer: FlashInfer is a kernel library for serving large language models. See the comparison table for live GitHub stats and shared categories.
- When should I choose REST over flashinfer?
- Choose REST over flashinfer when REST is primarily C; flashinfer is Cuda; Tags unique to REST: retrieval, speculative-decoding; Use REST when you need to improve the efficiency of large language model inference through retrieval-based speculative decoding.
- When should I choose flashinfer over REST?
- Choose flashinfer over REST when flashinfer is primarily Cuda; REST is C; 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 avoid REST?
- Avoid REST if your project does not require improvements in the efficiency of large language model inference. Do not use REST if your project is not compatible with the C language or if you prefer a different license than Apache-2.0.
- 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.
- Is REST or flashinfer more popular on GitHub?
- flashinfer has more GitHub stars (6,452 vs 220). Stars measure visibility, not whether either tool fits your constraints.
- Are REST and flashinfer open source?
- Yes - both are open-source projects on GitHub (REST: Apache-2.0, flashinfer: Apache-2.0).
- Where can I find alternatives to REST or flashinfer?
- GraphCanon lists graph-backed alternatives at REST alternatives and flashinfer alternatives (REST markdown twin, flashinfer 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, REST or flashinfer?
- REST: Slowing. flashinfer: 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 REST and flashinfer?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: REST trust report; flashinfer trust report.