Raises estimated decode speed by about 157%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Codestral 22B needs ~19.5 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q4_K_M quantization, expect ~35 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
37.5 tok/s
TTFT
5164 ms
Safe context
33K
Memory
19.5 GB / 24.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 34.9 tok/s | 3028 ms | 33K |
| Coding | B | Runs well | 34.9 tok/s | 5552 ms | 33K |
| Agentic Coding | B | Tight fit | 34.9 tok/s | 8075 ms | 33K |
| Reasoning | B | Runs well | 34.9 tok/s | 6561 ms | 33K |
| RAG | B | Tight fit | 34.9 tok/s | 10094 ms | 33K |
How Codestral 22B (22B params) fits at each quantization level on NVIDIA A10 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | B58 |
Q3_K_S | 3 | 10.8 GB | Low | B60 |
NVFP4 | 4 |
Copy-paste commands to run Codestral 22B on your machine.
Run
ollama run codestralUpgrade options
Raises estimated decode speed by about 157%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 61%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, NVIDIA A10 24GB can run Codestral 22B with a B grade (Runs well). Expected decode speed: 34.9 tok/s.
Codestral 22B (22B parameters) requires approximately 19.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Codestral 22B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA A10 24GB, Codestral 22B achieves approximately 34.9 tokens per second decode speed with a time-to-first-token of 5552ms using Q4_K_M quantization.
For coding workloads, Codestral 22B on NVIDIA A10 24GB receives a B grade with 34.9 tok/s and 33K context.
On NVIDIA A10 24GB, Codestral 22B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/codestral-22b-on-a10-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
12.3 GB |
| Medium |
| B60 |
Q4_K_M | 4 | 13.4 GB | Medium | B60 |
Q5_K_M | 5 | 15.8 GB | High | B60 |
Q6_KBest for your GPU | 6 | 18.0 GB | High | B59 |
Q8_0 | 8 | 23.5 GB | Very High | F0 |
F16 | 16 | 45.1 GB | Maximum | F0 |