Mistral Small 3.2 24B Instruct 2506 needs ~31.2 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~177 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
176.9 tok/s
TTFT
1094 ms
Safe context
567K
Memory
31.2 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 176.9 tok/s | 597 ms | 567K |
| Coding | C | Runs well | 176.9 tok/s | 1094 ms | 567K |
| Agentic Coding | C | Runs well | 176.9 tok/s | 1592 ms | 567K |
| Reasoning | C | Runs well | 176.9 tok/s | 1293 ms | 567K |
| RAG | C | Runs well | 176.9 tok/s | 1990 ms | 567K |
How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | D38 |
Q3_K_S | 3 | 11.8 GB | Low | D39 |
NVFP4 | 4 |
Copy-paste commands to run Mistral Small 3.2 24B Instruct 2506 on your machine.
Run
lms load hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf && lms server startYes, Gaudi 3 128GB can run Mistral Small 3.2 24B Instruct 2506 with a C grade (Runs well). Expected decode speed: 176.9 tok/s.
Mistral Small 3.2 24B Instruct 2506 (24B parameters) requires approximately 31.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Small 3.2 24B Instruct 2506 is Q4_K_M, which balances quality and memory efficiency.
On Gaudi 3 128GB, Mistral Small 3.2 24B Instruct 2506 achieves approximately 176.9 tokens per second decode speed with a time-to-first-token of 1094ms using Q4_K_M quantization.
For coding workloads, Mistral Small 3.2 24B Instruct 2506 on Gaudi 3 128GB receives a C grade with 176.9 tok/s and 567K context.
On Gaudi 3 128GB, Mistral Small 3.2 24B Instruct 2506 can safely use up to 567K 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-unsloth--mistral-small-3-2-24b-instruct-2506-gguf-on-gaudi-3-128gb" 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 |
| D39 |
Q4_K_M | 4 | 14.6 GB | Medium | D39 |
Q5_K_M | 5 | 17.3 GB | High | D39 |
Q6_K | 6 | 19.7 GB | High | D39 |
Q8_0 | 8 | 25.7 GB | Very High | D40 |
F16Best for your GPU | 16 | 49.2 GB | Maximum | C44 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.