Comparison
mistral.rs vs exllama
Verdict
Pick mistral.rs if mistral.rs is ideal for developers requiring fast and flexible LLM inference with support across multiple platforms. It provides prebuilt binaries and a simple installation process; 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 · mistral.rs alternatives · exllama alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | mistral.rs | exllama |
|---|---|---|
| Maintenance | Active (8d since push) As of 2w · github_public_v1 | Dormant (1041d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · 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
- mistral.rs
- Fast flexible LLM inference
- exllama
- Memory-efficient rewrite of HF transformers for Llama with quantized weights
Stars
- mistral.rs
- 7.6k
- exllama
- 2.9k
Forks
- mistral.rs
- 671
- exllama
- 220
Open issues
- mistral.rs
- 380
- exllama
- 65
Language
- mistral.rs
- Rust
- exllama
- Python
Adopt for
- mistral.rs
- Mistral.rs is ideal for developers requiring fast and flexible LLM inference with support across multiple platforms. It provides prebuilt binaries and a simple installation process.
- 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
- mistral.rs
- -
- exllama
- -
Runtime
- mistral.rs
- -
- exllama
- -
License
- mistral.rs
- MIT
- exllama
- MIT
Last pushed
- mistral.rs
- Jul 29, 2026
- exllama
- Sep 30, 2023
Categories
- mistral.rs
- Inference & Serving
- exllama
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- mistral.rs
- Active (82%)
- exllama
- Dormant (18%)
Days since push
- mistral.rs
- 8d
- exllama
- 1041d
Open issues (now)
- mistral.rs
- 380
- exllama
- 65
OSV dependency advisories
- mistral.rs
- No lockfile (source not queried)
- exllama
- Published findings
Full report
- mistral.rs
- Trust report
- exllama
- Trust report
Choose mistral.rs if…
- mistral.rs is primarily Rust; exllama is Python.
- Tags unique to mistral.rs: llm, rust, uqff.
- Mistral.rs should be used when seeking Rust-based implementation that supports quick and flexible deployment of large language models, particularly on Linux, macOS, or Windows systems
When NOT to use mistral.rs
- Avoid Mistral.rs if your project is strictly dependent on another programming language framework as it is implemented in Rust
- If needing tight control over model-specific optimizations not provided by default prebuild paths, then consider alternatives with extensive fine-tuning options out-of-the-box
Choose exllama if…
- exllama is primarily Python; mistral.rs is Rust.
- Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu.
- Also covers LLM Frameworks.
- - 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 (EricLBuehler/mistral.rs) · observed Aug 7, 2026
- GitHub forks (EricLBuehler/mistral.rs) · observed Aug 7, 2026
- Last push (EricLBuehler/mistral.rs) · observed Jul 29, 2026
- License file (MIT) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 14, 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: mistral.rs 7.6k · exllama 2.9k (synced Aug 7, 2026).
Common questions
- What is the difference between mistral.rs and exllama?
- mistral.rs: Fast flexible LLM inference. 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 mistral.rs over exllama?
- Choose mistral.rs over exllama when mistral.rs is primarily Rust; exllama is Python; Tags unique to mistral.rs: llm, rust, uqff; Mistral.rs should be used when seeking Rust-based implementation that supports quick and flexible deployment of large language models, particularly on Linux, macOS, or Windows systems.
- When should I choose exllama over mistral.rs?
- Choose exllama over mistral.rs when exllama is primarily Python; mistral.rs is Rust; Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; Also covers LLM Frameworks; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
- When should I avoid mistral.rs?
- Avoid Mistral.rs if your project is strictly dependent on another programming language framework as it is implemented in Rust If needing tight control over model-specific optimizations not provided by default prebuild paths, then consider alternatives with extensive fine-tuning options out-of-the-box
- 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 mistral.rs or exllama more popular on GitHub?
- mistral.rs has more GitHub stars (7,575 vs 2,937). Stars measure visibility, not whether either tool fits your constraints.
- Are mistral.rs and exllama open source?
- Yes - both are open-source projects on GitHub (mistral.rs: MIT, exllama: MIT).
- Where can I find alternatives to mistral.rs or exllama?
- GraphCanon lists graph-backed alternatives at mistral.rs alternatives and exllama alternatives (mistral.rs 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, mistral.rs or exllama?
- mistral.rs: Active. 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 mistral.rs and exllama?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: mistral.rs trust report; exllama trust report.