Codestral-22B-v0.1 GGUF size and VRAM requirements
Codestral-22B-v0.1 is a 22.25 billion parameter AI model developed by Mistral AI, designed for coding-related tasks such as code generation, analysis, and debugging. It is part of the Codestral series, optimized to assist with programming languages and software development workflows. The model is based on the open-source framework provided by the Hugging Face platform.
To run bartowski/Codestral-22B-v0.1-GGUF locally at a 4,096-token context, its quantized versions need between 7.86 GB (IQ2_XS, lowest quality) and 84.55 GB (F32, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is IQ2_XS, needing about 7.86 GB. That means bartowski/Codestral-22B-v0.1-GGUF fits entirely in the VRAM of an 8 GB GPU or larger, running fully on the GPU.
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| IQ2_XS | 2.39 | Very low | 6.19 GB | 0.88 GB | 7.86 GB | 64.6 t/s | Fits in VRAM |
| IQ2_S | 2.53 | Very low | 6.55 GB | 0.88 GB | 8.23 GB | 7.6 t/s | Offload |
| IQ2_M | 2.74 | Low | 7.1 GB | 0.88 GB | 8.77 GB | 7.0 t/s | Offload |
| Q2_K | 2.97 | Low | 7.7 GB | 0.88 GB | 9.38 GB | 6.5 t/s | Offload |
| IQ3_XXS | 3.09 | Low | 8.01 GB | 0.88 GB | 9.68 GB | 6.2 t/s | Offload |
| IQ3_XS | 3.3 | Low | 8.55 GB | 0.88 GB | 10.22 GB | 5.9 t/s | Offload |
| Q3_K_S | 3.47 | Fair | 8.98 GB | 0.88 GB | 10.65 GB | 5.6 t/s | Offload |
| IQ3_M | 3.62 | Fair | 9.37 GB | 0.88 GB | 11.05 GB | 5.3 t/s | Offload |
| Q3_K_M | 3.87 | Fair | 10.02 GB | 0.88 GB | 11.69 GB | 5.0 t/s | Offload |
| Q3_K_L | 4.22 | Good | 10.92 GB | 0.88 GB | 12.6 GB | 4.6 t/s | Offload |
| IQ4_XS | 4.29 | Good | 11.12 GB | 0.88 GB | 12.79 GB | 4.5 t/s | Offload |
| Q4_K_S | 4.55 | Good | 11.79 GB | 0.88 GB | 13.47 GB | 4.2 t/s | Offload |
| Q4_K_M | 4.8 | Good | 12.42 GB | 0.88 GB | 14.1 GB | 4.0 t/s | Offload |
| Q5_K_S | 5.51 | Very good | 14.27 GB | 0.88 GB | 15.95 GB | 3.5 t/s | Offload |
| Q5_K_M | 5.65 | Very good | 14.64 GB | 0.88 GB | 16.32 GB | 3.4 t/s | Offload |
| Q6_K | 6.56 | Excellent | 17.0 GB | 0.88 GB | 18.67 GB | 2.9 t/s | Offload |
| Q8_0 | 8.5 | Excellent | 22.02 GB | 0.88 GB | 23.69 GB | 2.3 t/s | Offload |
| F32 | 32.0 | Excellent | 82.88 GB | 0.88 GB | 84.55 GB | — | Insufficient |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run bartowski/Codestral-22B-v0.1-GGUF?
You need about 7.86 GB of VRAM to run bartowski/Codestral-22B-v0.1-GGUF entirely on the GPU using the IQ2_XS quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run bartowski/Codestral-22B-v0.1-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run bartowski/Codestral-22B-v0.1-GGUF fully on the GPU using IQ2_XS (about 7.86 GB).
Can I run bartowski/Codestral-22B-v0.1-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run bartowski/Codestral-22B-v0.1-GGUF fully on the GPU using Q5_K_S (about 15.95 GB).
Can I run bartowski/Codestral-22B-v0.1-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run bartowski/Codestral-22B-v0.1-GGUF fully on the GPU using Q8_0 (about 23.69 GB).
What is the best quantization for bartowski/Codestral-22B-v0.1-GGUF?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.