Comparison
tvm vs awesome-tensor-compilers
Verdict
Pick tvm if apache TVM stands out for its python-driven approach towards ML compilation and universal deployment options; pick awesome-tensor-compilers if decision-critical Facts for awesome-tensor-compilers.
Markdown twin · tvm alternatives · awesome-tensor-compilers alternatives
GraphCanon updated Oct 4, 2026
11views this month
vs
Trust & integrity
| Signal | tvm | awesome-tensor-compilers |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 30, 2026 · github_public_v1 | Dormant (714d since push) As of Oct 4, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Personal account As of Oct 4, 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
- tvm
- Open Machine Learning Compiler Framework
- awesome-tensor-compilers
- A collection of compiler projects and papers for tensor computation and deep learning.
Stars
- tvm
- 14k
- awesome-tensor-compilers
- 2.8k
Forks
- tvm
- 4.0k
- awesome-tensor-compilers
- 330
Open issues
- tvm
- 281
- awesome-tensor-compilers
- 5
Language
- tvm
- Python
- awesome-tensor-compilers
- -
Adopt for
- tvm
- Apache TVM stands out for its python-driven approach towards ML compilation and universal deployment options.
- awesome-tensor-compilers
- Decision-critical Facts for awesome-tensor-compilers
Persona
- tvm
- -
- awesome-tensor-compilers
- -
Runtime
- tvm
- -
- awesome-tensor-compilers
- -
License
- tvm
- Apache-2.0
- awesome-tensor-compilers
- -
Last pushed
- tvm
- Sep 30, 2026
- awesome-tensor-compilers
- Oct 19, 2024
Categories
- tvm
- Inference & Serving, LLM Frameworks, Model Training
- awesome-tensor-compilers
- Inference & Serving, Model Training
Trust and health
Maintenance
- tvm
- Very active (96%)
- awesome-tensor-compilers
- Dormant (18%)
Days since push
- tvm
- 0d
- awesome-tensor-compilers
- 714d
Open issues (now)
- tvm
- 281
- awesome-tensor-compilers
- 5
Stars delta
- tvm
- +144 (30d)
- awesome-tensor-compilers
- +18 (30d)
Open issues delta
- tvm
- +70 (30d)
- awesome-tensor-compilers
- +1 (30d)
Owner type
- tvm
- Organization
- awesome-tensor-compilers
- User
Full report
- tvm
- Trust report
- awesome-tensor-compilers
- Trust report
Choose tvm if…
- Tags unique to tvm: gpu, javascript, metal, opencl.
- Also covers LLM Frameworks.
- When you focus on Python-first customization to quickly prototype and iterate machine learning models and compilers.
When NOT to use tvm
- Avoid if your workflow demands an immutable model pipeline; TVM shines in flexibility but might be overkill for static workload scenarios.
- For projects that strictly adhere to one hardware platform or API set, as the universal support of TVM could introduce unnecessary complexity.
Choose awesome-tensor-compilers if…
- Tags unique to awesome-tensor-compilers: code-generation, high-performance-computing, programming-language, tensor.
- If you need references to papers on cost models and automated optimizations for tensor computation.
- Leaner open-issue backlog (5).
When NOT to use awesome-tensor-compilers
- Avoid if focused solely on implementation without the need for theoretical background or detailed optimization methods.
- Not suitable if your project requires immediate integration of a specific tensor compiler technology rather than review of existing research.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (apache/tvm) · observed Sep 30, 2026
- GitHub forks (apache/tvm) · observed Sep 30, 2026
- Last push (apache/tvm) · observed Sep 30, 2026
- License file (Apache-2.0) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (merrymercy/awesome-tensor-compilers) · observed Oct 4, 2026
- GitHub forks (merrymercy/awesome-tensor-compilers) · observed Oct 4, 2026
- Last push (merrymercy/awesome-tensor-compilers) · observed Oct 19, 2024
- License file (unknown) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: tvm 14k · awesome-tensor-compilers 2.8k (synced Sep 30, 2026).
Common questions
- What is the difference between tvm and awesome-tensor-compilers?
- tvm: Open Machine Learning Compiler Framework. awesome-tensor-compilers: A collection of compiler projects and papers for tensor computation and deep learning.. See the comparison table for live GitHub stats and shared categories.
- When should I choose tvm over awesome-tensor-compilers?
- Choose tvm over awesome-tensor-compilers when Tags unique to tvm: gpu, javascript, metal, opencl; Also covers LLM Frameworks; When you focus on Python-first customization to quickly prototype and iterate machine learning models and compilers.
- When should I choose awesome-tensor-compilers over tvm?
- Choose awesome-tensor-compilers over tvm when Tags unique to awesome-tensor-compilers: code-generation, high-performance-computing, programming-language, tensor; If you need references to papers on cost models and automated optimizations for tensor computation; Leaner open-issue backlog (5).
- When should I avoid tvm?
- Avoid if your workflow demands an immutable model pipeline; TVM shines in flexibility but might be overkill for static workload scenarios. For projects that strictly adhere to one hardware platform or API set, as the universal support of TVM could introduce unnecessary complexity.
- When should I avoid awesome-tensor-compilers?
- Avoid if focused solely on implementation without the need for theoretical background or detailed optimization methods. Not suitable if your project requires immediate integration of a specific tensor compiler technology rather than review of existing research.
- Is tvm or awesome-tensor-compilers more popular on GitHub?
- tvm has more GitHub stars (13,786 vs 2,788). Stars measure visibility, not whether either tool fits your constraints.
- Are tvm and awesome-tensor-compilers open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to tvm or awesome-tensor-compilers?
- GraphCanon lists graph-backed alternatives at tvm alternatives and awesome-tensor-compilers alternatives (tvm markdown twin, awesome-tensor-compilers 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, tvm or awesome-tensor-compilers?
- tvm: Very active. awesome-tensor-compilers: Dormant. 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 tvm and awesome-tensor-compilers?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: tvm trust report; awesome-tensor-compilers trust report.