Can AMD Radeon RX 7900 XTX run Codestral 22B?

22B parameter Code model on 24GB GDDR6

Yes — runs at 8-bit quantization
~17-21 tok/sUsable
SpeedFast inference, near-native speed
QualityNear-lossless — virtually identical to FP16
AMD GPUs lack CUDA. While Codestral 22B can technically run via llama.cpp/GGUF, the setup is more complex and less optimized than on NVIDIA hardware.

VRAM Requirements

Codestral 22B is a 22B parameter model. At full precision (FP16), it requires 44GB of VRAM. Your AMD Radeon RX 7900 XTX has 24GB, so you'll need to quantize it to 8-bit (Q8) to fit.

FP16 (Full Precision)44GB (need 20GB more)

Maximum quality, no quantization

Q8 (8-bit)22GB (2GB free)

Near-lossless, ~50% size reduction

Q4 (4-bit)13GB (11GB free)

Good quality, ~75% size reduction

Your GPU VRAM: 24GB GDDR6 at 960 GB/s bandwidth
Recommended system RAM: 48GB DDR5 (2x GPU VRAM minimum for model overflow)

What This Means in Practice

Codestral 22B at this precision on AMD Radeon RX 7900 XTX gives excellent code completion and generation. Fast enough for real-time IDE integration. Handles complex refactoring, multi-file edits, and long-context code understanding.

How to Set It Up

Step 1: Install Ollama

curl -fsSL https://ollama.com/install.sh | sh

Ollama is the easiest way to run local LLMs. Works on Linux, macOS, and Windows.

Step 2: Download and run Codestral 22B

ollama run codestral:22b

This downloads the model (~22GB). First run takes a few minutes.

Step 3: Verify GPU is being used

rocm-smi

Check that VRAM usage increases when the model loads. You should see ~22GB used.

AMD Radeon RX 7900 XTX Specs

VRAM24GB GDDR6
Memory Bandwidth960 GB/s
TDP355W
CUDA CoresN/A
Street Price~$850
AI Rating5/10

Other Code Models on AMD Radeon RX 7900 XTX

About Codestral 22B

Top code completion model. Q4 fits on 16GB GPUs.

Category: Code · Parameters: 22B · CUDA required: No (runs via llama.cpp/GGUF)