Comparison
yalm vs exllama
Verdict
Pick yalm if yALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries; pick exllama if exLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards.
Markdown twin · yalm alternatives · exllama alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | yalm | exllama |
|---|---|---|
| Maintenance | Slowing (315d since push) As of 1mo · github_public_v1 | Dormant (1041d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1mo · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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
- yalm
- LLM inference engine in C++/CUDA without dependency on external libraries except for I/O
- exllama
- Memory-efficient rewrite of HF transformers for Llama with quantized weights
Stars
- yalm
- 592
- exllama
- 2.9k
Forks
- yalm
- 64
- exllama
- 220
Open issues
- yalm
- 4
- exllama
- 65
Language
- yalm
- C++
- exllama
- Python
Adopt for
- yalm
- YALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries.
- exllama
- ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards.
Persona
- yalm
- -
- exllama
- -
Runtime
- yalm
- -
- exllama
- -
License
- yalm
- -
- exllama
- MIT
Last pushed
- yalm
- Sep 13, 2025
- exllama
- Sep 30, 2023
Categories
- yalm
- Inference & Serving
- exllama
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- yalm
- Slowing (36%)
- exllama
- Dormant (18%)
Days since push
- yalm
- 315d
- exllama
- 1041d
Open issues (now)
- yalm
- 4
- exllama
- 65
OSV dependency advisories
- yalm
- No lockfile (source not queried)
- exllama
- Published findings
Full report
- yalm
- Trust report
- exllama
- Trust report
Choose yalm if…
- yalm is primarily C++; exllama is Python.
- Tags unique to yalm: cpp, cuda, llm-inference, machine-learning.
- When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies
When NOT to use yalm
- If extensive functionality or ease of use from other ML libraries is required, as YALM does not support dependencies beyond I/O needs
- For developers who prefer tools with broader community support and more comprehensive feature sets, given that YALM specializes in a narrow scope
Choose exllama if…
- exllama is primarily Python; yalm is C++.
- Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu.
- Also covers LLM Frameworks.
- exllama ships Docker support for self-hosted deployment.
- - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
When NOT to use exllama
- - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better.
- - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts).
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (andrewkchan/yalm) · observed Jul 25, 2026
- GitHub forks (andrewkchan/yalm) · observed Jul 25, 2026
- Last push (andrewkchan/yalm) · observed Sep 13, 2025
- License file (unknown) · observed Jul 25, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (turboderp/exllama) · observed Aug 7, 2026
- GitHub forks (turboderp/exllama) · observed Aug 7, 2026
- Last push (turboderp/exllama) · observed Sep 30, 2023
- License file (MIT) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: yalm 592 · exllama 2.9k (synced Jul 25, 2026).
Common questions
- What is the difference between yalm and exllama?
- yalm: LLM inference engine in C++/CUDA without dependency on external libraries except for I/O. exllama: Memory-efficient rewrite of HF transformers for Llama with quantized weights. See the comparison table for live GitHub stats and shared categories.
- When should I choose yalm over exllama?
- Choose yalm over exllama when yalm is primarily C++; exllama is Python; Tags unique to yalm: cpp, cuda, llm-inference, machine-learning; When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies.
- When should I choose exllama over yalm?
- Choose exllama over yalm when exllama is primarily Python; yalm is C++; Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; Also covers LLM Frameworks; exllama ships Docker support for self-hosted deployment; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
- When should I avoid yalm?
- If extensive functionality or ease of use from other ML libraries is required, as YALM does not support dependencies beyond I/O needs For developers who prefer tools with broader community support and more comprehensive feature sets, given that YALM specializes in a narrow scope
- When should I avoid exllama?
- - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts).
- Is yalm or exllama more popular on GitHub?
- exllama has more GitHub stars (2,937 vs 592). Stars measure visibility, not whether either tool fits your constraints.
- Are yalm and exllama open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to yalm or exllama?
- GraphCanon lists graph-backed alternatives at yalm alternatives and exllama alternatives (yalm markdown twin, exllama 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, yalm or exllama?
- yalm: Slowing. exllama: 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 yalm and exllama?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: yalm trust report; exllama trust report.