---
title: "mistral.rs vs exllama"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/ericlbuehler-mistral-rs-vs-turboderp-exllama"
tools: ["ericlbuehler-mistral-rs", "turboderp-exllama"]
---

# mistral.rs vs exllama

*GraphCanon updated Aug 7, 2026*

## 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.

[mistral.rs](https://github.com/EricLBuehler/mistral.rs) reports 7.6k GitHub stars, 671 forks, and 380 open issues, last pushed Jul 29, 2026. [exllama](https://github.com/turboderp/exllama) has 2.9k stars, 220 forks, and 65 open issues, last pushed Sep 30, 2023. Figures are from public GitHub metadata via [mistral.rs's repository](https://github.com/EricLBuehler/mistral.rs) and [exllama's repository](https://github.com/turboderp/exllama).

| | [mistral.rs](/tools/ericlbuehler-mistral-rs.md) | [exllama](/tools/turboderp-exllama.md) |
| --- | --- | --- |
| Tagline | Fast flexible LLM inference | Memory-efficient rewrite of HF transformers for Llama with quantized weights |
| Stars | 7,575 | 2,937 |
| Forks | 671 | 220 |
| Open issues | 380 | 65 |
| Language | Rust | Python |
| Adopt for | 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 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 | - | - |
| Runtime | - | - |
| License | MIT | MIT |
| Categories | Inference & Serving | Inference & Serving, LLM Frameworks |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [mistral.rs](/tools/ericlbuehler-mistral-rs.md) | [exllama](/tools/turboderp-exllama.md) |
| --- | --- | --- |
| Maintenance | Active (82%) | Dormant (18%) |
| Days since push | 8d | 1041d |
| Open issues (now) | 380 | 65 |
| Full report | [trust report](/tools/ericlbuehler-mistral-rs/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) |

## Decision facts: mistral.rs

- **Adopt for:** 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.

## Decision facts: exllama

- **Adopt for:** 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.

## Choose when

### 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

### 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 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 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).

## 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](/tools/ericlbuehler-mistral-rs/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([mistral.rs markdown twin](/tools/ericlbuehler-mistral-rs/alternatives.md), [exllama markdown twin](/tools/turboderp-exllama/alternatives.md)), 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](/compare/ericlbuehler-mistral-rs-vs-turboderp-exllama.md) 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](/tools/ericlbuehler-mistral-rs/trust); [exllama trust report](/tools/turboderp-exllama/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=ericlbuehler-mistral-rs`](/api/graphcanon/graph?tool=ericlbuehler-mistral-rs)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_