Mistral Small 24B Instruct 2501 needs ~28.3 GB VRAM. RTX PRO 6000 Blackwell Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~103 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
102.8 tok/s
TTFT
1883 ms
Safe context
401K
Memory
28.3 GB / 96.0 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 | C | Runs well | 102.8 tok/s | 1027 ms | 401K |
| Coding | C | Runs well | 102.8 tok/s | 1883 ms | 401K |
| Agentic Coding | C | Runs well | 102.8 tok/s | 2739 ms | 401K |
| Reasoning | C | Runs well | 102.8 tok/s | 2225 ms | 401K |
| RAG | C | Runs well | 102.8 tok/s | 3423 ms | 401K |
How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on RTX PRO 6000 Blackwell Workstation Edition 96GB (96.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | D39 |
Q3_K_S | 3 | 11.8 GB | Low | D40 |
NVFP4 | 4 | 13.4 GB | Medium | D40 |
Q4_K_M | 4 | 14.6 GB | Medium | D40 |
Q5_K_M | 5 | 17.3 GB | High | C40 |
Q6_K | 6 | 19.7 GB | High | C41 |
Q8_0 | 8 | 25.7 GB | Very High | C41 |
F16Best for your GPU | 16 | 49.2 GB | Maximum | C47 |
Copy-paste commands to run Mistral Small 24B Instruct 2501 on your machine.
Run
lms load hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf && lms server startYes, RTX PRO 6000 Blackwell Workstation Edition 96GB can run Mistral Small 24B Instruct 2501 with a C grade (Runs well). Expected decode speed: 102.8 tok/s.
Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 28.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Small 24B Instruct 2501 is Q4_K_M, which balances quality and memory efficiency.
On RTX PRO 6000 Blackwell Workstation Edition 96GB, Mistral Small 24B Instruct 2501 achieves approximately 102.8 tokens per second decode speed with a time-to-first-token of 1883ms using Q4_K_M quantization.
For coding workloads, Mistral Small 24B Instruct 2501 on RTX PRO 6000 Blackwell Workstation Edition 96GB receives a C grade with 102.8 tok/s and 401K context.
On RTX PRO 6000 Blackwell Workstation Edition 96GB, Mistral Small 24B Instruct 2501 can safely use up to 401K tokens of context. The model's official context limit is —, 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/hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf-on-rtx-pro-6000-blackwell-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: