GPUs/NVIDIA A100 40GB/Codestral 22B

Can NVIDIA A100 40GB run Codestral 22B?

22B parameter Code model on 40GB HBM2e

Yes — runs at 8-bit quantization
~45-55 tok/sFast
SpeedFast inference, near-native speed
QualityNear-lossless — virtually identical to FP16

VRAM Requirements

Codestral 22B is a 22B parameter model. At full precision (FP16), it requires 44GB of VRAM. Your NVIDIA A100 40GB has 40GB, so you'll need to quantize it to 8-bit (Q8) to fit.

FP16 (Full Precision)44GB (need 4GB more)

Maximum quality, no quantization

Q8 (8-bit)22GB (18GB free)

Near-lossless, ~50% size reduction

Q4 (4-bit)13GB (27GB free)

Good quality, ~75% size reduction

Your GPU VRAM: 40GB HBM2e at 1555 GB/s bandwidth
Recommended system RAM: 80GB DDR5 (2x GPU VRAM minimum for model overflow)

What This Means in Practice

Codestral 22B at this precision on NVIDIA A100 40GB 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

nvidia-smi

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

NVIDIA A100 40GB Specs

VRAM40GB HBM2e
Memory Bandwidth1555 GB/s
TDP250W
CUDA Cores6,912
Street Price~$4500
AI Rating10/10

Other Code Models on NVIDIA A100 40GB

About Codestral 22B

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

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