Can NVIDIA RTX 4000 Ada run Codestral 22B?

22B parameter Code model on 20GB GDDR6

Yes — runs at 4-bit quantization
~17-22 tok/sUsable
SpeedModerate speed, usable for interactive chat
QualityGood quality with slight degradation on complex reasoning

VRAM Requirements

Codestral 22B is a 22B parameter model. At full precision (FP16), it requires 44GB of VRAM. Your NVIDIA RTX 4000 Ada has 20GB, so you'll need to quantize it to 4-bit (Q4) to fit.

FP16 (Full Precision)44GB (need 24GB more)

Maximum quality, no quantization

Q8 (8-bit)22GB (need 2GB more)

Near-lossless, ~50% size reduction

Q4 (4-bit)13GB (7GB free)

Good quality, ~75% size reduction

Your GPU VRAM: 20GB GDDR6 at 360 GB/s bandwidth
Recommended system RAM: 40GB DDR5 (2x GPU VRAM minimum for model overflow)

What This Means in Practice

Codestral 22B at Q4 on NVIDIA RTX 4000 Ada works for code completion but complex multi-file operations may show quality drops. Still very usable for day-to-day coding assistance. Consider a larger VRAM GPU for professional code generation workflows.

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:q4_K_M

This downloads the Q4_K_M quantized version (~13GB). First run takes a few minutes to download.

Step 3: Verify GPU is being used

nvidia-smi

Check that VRAM usage increases when the model loads. You should see ~13GB used.

NVIDIA RTX 4000 Ada Specs

VRAM20GB GDDR6
Memory Bandwidth360 GB/s
TDP130W
CUDA Cores6,144
Street Price~$1100
AI Rating7/10

Other Code Models on NVIDIA RTX 4000 Ada

About Codestral 22B

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

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