Codestral — local AI model by Mistral AI

Written by Jakub Rusinowski · Last updated

Superseded model. Codestral has been superseded by Devstral. This page is kept for reference; the newer family is a better starting point. View Devstral →

Mistral's dedicated code generation model, trained on a massive programming corpus covering 80+ programming languages. Excels at fill-in-the-middle (FIM) completion — the key technique powering IDE autocomplete. Integrates natively with VS Code via continue.dev and Cursor.

Mistral AI Hugging Face

Variants

The smallest Codestral variant needs about 14 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache.

ModelVRAM
Codestral 22B →
22B
~14.2 GB

Memory is quantized weights plus overhead at Q4_K_M, from the same engine as the GPU & VRAM checker.

How to run Codestral locally

Install Ollama, then pull the tag.

ollama run codestral:22b

Pick a size above for its own VRAM figure, speed estimate and install command.

Licence

MNPL-0.1Research / non-commercial only

Research / non-commercial only — this licence does NOT permit shipping a commercial product.

Applies to: Codestral 22B

Recommended GPU

The cheapest catalogued GPU that runs Codestral locally (min 14 GB VRAM) is the AMD Radeon RX 9060 XT 16GB (16 GB).

Affiliate disclosure: Some links on this page are affiliate links — if you buy through them, LLM Configurator may earn a commission at no extra cost to you. As an Amazon Associate, LLM Configurator earns from qualifying purchases.
AMD Radeon RX 9060 XT 16GB
16 GB VRAM · 160 W board power
2026 prices are volatile — check the current listing.

Can I run Codestral on my GPU?

Codestral — frequently asked questions

How much VRAM does Codestral need?

Codestral needs about 14 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Codestral 22B (14 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.

Can I run Codestral on an RTX 4090 (24 GB)?

Yes — Codestral runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.

What quantization should I use for Codestral?

Q4_K_M is the best balance of quality and VRAM for Codestral in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.

How do I run Codestral with Ollama?

Install Ollama, then run: ollama run codestral:22b. This downloads Codestral and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.