Mistral Small 3.2 24B needs ~20.7 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~15 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
Tight fit
Decode
15.0 tok/s
TTFT
12915 ms
Safe context
38K
Memory
20.7 GB / 24.0 GB
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 13.9 tok/s | 7573 ms | 38K |
| Coding | A | Tight fit | 15.0 tok/s | 12915 ms | 38K |
| Agentic Coding | A | Runs with offload | 15.0 tok/s | 18786 ms | 38K |
| Reasoning | A | Tight fit | 15.0 tok/s | 15264 ms | 38K |
| RAG | A | Runs with offload | 15.0 tok/s | 23483 ms | 38K |
How Mistral Small 3.2 24B (24B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | A83 |
Q3_K_S | 3 | 11.8 GB | Low | A84 |
NVFP4 | 4 |
Copy-paste commands to run Mistral Small 3.2 24B on your machine.
Run
ollama run mistral-small3.2Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 30.5B | S | 30.9 tok/s | ||
| 27B | S | 13.4 tok/s |
Yes, Tesla P40 24GB can run Mistral Small 3.2 24B with a A grade (Tight fit). Expected decode speed: 15.0 tok/s.
Mistral Small 3.2 24B (24B parameters) requires approximately 20.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Small 3.2 24B is Q4_K_M, which balances quality and memory efficiency.
On Tesla P40 24GB, Mistral Small 3.2 24B achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12915ms using Q4_K_M quantization.
For coding workloads, Mistral Small 3.2 24B on Tesla P40 24GB receives a A grade with 15.0 tok/s and 38K context.
On Tesla P40 24GB, Mistral Small 3.2 24B can safely use up to 38K tokens of context. The model's official context limit is 131K, 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/mistral-small-3.2-24b-on-tesla-p40-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
13.4 GB |
| Medium |
| A84 |
Q4_K_M | 4 | 14.6 GB | Medium | A84 |
Q5_K_MBest for your GPU | 5 | 17.3 GB | High | A83 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
| 27B | S | 13.4 tok/s |
| 30B | S | 31.9 tok/s |
| 35B | A | 16.7 tok/s |