Mistral Small 3.2 24B needs ~20.3 GB VRAM. RTX 4000 Ada 20GB has 20.0 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
0.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
15.0 tok/s
TTFT
12893 ms
Safe context
14K
Memory
20.3 GB / 20.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload | 20.6 tok/s | 5122 ms | 14K |
| Coding | A | Runs with offload (needs ~0.2 GB host RAM) | 15.0 tok/s | 12893 ms | 14K |
| Agentic Coding | A | Very compromised (needs ~1.8 GB host RAM) | 11.8 tok/s | 23823 ms | 14K |
| Reasoning | A | Runs with offload (needs ~0.2 GB host RAM) | 15.0 tok/s | 15237 ms | 14K |
| RAG | A | Very compromised (needs ~1.8 GB host RAM) | 11.8 tok/s | 29778 ms | 14K |
How Mistral Small 3.2 24B (24B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | A85 |
Q3_K_S | 3 | 11.8 GB | Low | A84 |
NVFP4 | 4 | 13.4 GB | Medium | A84 |
Q4_K_MBest for your GPU | 4 | 14.6 GB | Medium | A84 |
Q5_K_M | 5 | 17.3 GB | High | F0 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
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 |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 23.2 tok/s | |
| Qwen 3.5 27B | 27B | A | 10.4 tok/s | |
| Qwen 3.6 27B | 27B | S | 13 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | A | 24.6 tok/s | |
| Qwen 3 30B A3B | 30.5B | A | 23.2 tok/s |
Yes, RTX 4000 Ada 20GB can run Mistral Small 3.2 24B with a A grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 15.0 tok/s.
Mistral Small 3.2 24B (24B parameters) requires approximately 20.3 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 RTX 4000 Ada 20GB, Mistral Small 3.2 24B achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12893ms using Q4_K_M quantization.
For coding workloads, Mistral Small 3.2 24B on RTX 4000 Ada 20GB receives a A grade with 15.0 tok/s and 14K context.
On RTX 4000 Ada 20GB, Mistral Small 3.2 24B can safely use up to 14K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mistral-small-3.2-24b-on-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: