Codestral-22B on NVIDIA RTX 3060 12GB (12GB VRAM)
Even in 4-bit Q4_K_M, Codestral-22B requires 16.27 GB VRAM (2x NVIDIA RTX 3060 12GB GPUs needed for full VRAM execution, or heavy CPU RAM offloading at ~2.1 tok/s).
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
16.27 GB / 12 GB🚀 Ready-to-Run Launch Command
ollama run codestral:22b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf mistralai/Codestral-22B-v0.1-GGUF -c 8192 -ngl 99 -fa
Need 16.27 GB+ VRAM to run Codestral-22B?
Recommended cloud setup for 4-bit: RTX 4090 (24GB) (or 48.14 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:
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All Quantization Formats: Codestral-22B on NVIDIA RTX 3060 12GB
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x NVIDIA RTX 3060 12GB (12GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 45.73 GB | 1.75 GB | 48.14 GB | No (Over by 36.1GB) | ~1.2 tok/s | 5x NVIDIA RTX 3060 12GB |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 24.3 GB | 1.75 GB | 26.71 GB | No (Over by 14.7GB) | ~1.2 tok/s | 3x NVIDIA RTX 3060 12GB |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 24.3 GB | 1.75 GB | 26.71 GB | No (Over by 14.7GB) | ~1.2 tok/s | 3x NVIDIA RTX 3060 12GB |
| GGUF Q6_K (6-bit) | 6.56 bpw | 18.75 GB | 1.75 GB | 21.16 GB | No (Over by 9.2GB) | ~1.6 tok/s | 2x NVIDIA RTX 3060 12GB |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 16.26 GB | 1.75 GB | 18.67 GB | No (Over by 6.7GB) | ~1.8 tok/s | 2x NVIDIA RTX 3060 12GB |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 13.86 GB | 1.75 GB | 16.27 GB | No (Over by 4.3GB) | ~2.1 tok/s | 2x NVIDIA RTX 3060 12GB |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 12.15 GB | 1.75 GB | 14.56 GB | No (Over by 2.6GB) | ~2.4 tok/s | 2x NVIDIA RTX 3060 12GB |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 11.18 GB | 1.75 GB | 13.59 GB | No (Over by 1.6GB) | ~2.7 tok/s | 2x NVIDIA RTX 3060 12GB |
Frequently Asked Questions (Codestral-22B + NVIDIA RTX 3060 12GB)
Can NVIDIA RTX 3060 12GB (12GB) run Codestral-22B locally?
No — A single NVIDIA RTX 3060 12GB (12GB) cannot fit Codestral-22B in VRAM. Even in 4-bit Q4_K_M, Codestral-22B requires 16.27 GB VRAM (2x NVIDIA RTX 3060 12GB GPUs needed for full VRAM execution, or heavy CPU RAM offloading at ~2.1 tok/s).
How is KV Cache calculated for Codestral-22B?
Codestral-22B uses Dense-GQA with 56 transformer layers, 48 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 1.75 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 0.88 GB.