Magistral Small 2507 needs ~30.8 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~190 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
190.2 tok/s
TTFT
1018 ms
Safe context
131K
Memory
30.8 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 | S | Runs well | 190.2 tok/s | 555 ms | 131K |
| Coding | S | Runs well | 190.2 tok/s | 1018 ms | 131K |
| Agentic Coding | S | Runs well | 190.2 tok/s | 1481 ms | 131K |
| Reasoning | S | Runs well | 190.2 tok/s | 1203 ms | 131K |
| RAG | S | Runs well | 190.2 tok/s | 1851 ms | 131K |
How Magistral Small 2507 (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 | A80 |
Q3_K_S | 3 | 11.8 GB | Low | A80 |
NVFP4 | 4 |
Copy-paste commands to run Magistral Small 2507 on your machine.
Run
ollama run magistralYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 37.5 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S |
Yes, Gaudi 3 128GB can run Magistral Small 2507 with a S grade (Runs well). Expected decode speed: 190.2 tok/s.
Magistral Small 2507 (24B parameters) requires approximately 30.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Magistral Small 2507 is Q4_K_M, which balances quality and memory efficiency.
On Gaudi 3 128GB, Magistral Small 2507 achieves approximately 190.2 tokens per second decode speed with a time-to-first-token of 1018ms using Q4_K_M quantization.
For coding workloads, Magistral Small 2507 on Gaudi 3 128GB receives a S grade with 190.2 tok/s and 131K context.
On Gaudi 3 128GB, Magistral Small 2507 can safely use up to 131K 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/magistral-small-2507-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:
| Medium |
| A80 |
Q4_K_M | 4 | 14.6 GB | Medium | A80 |
Q5_K_M | 5 | 17.3 GB | High | A80 |
Q6_K | 6 | 19.7 GB | High | A81 |
Q8_0 | 8 | 25.7 GB | Very High | A81 |
F16Best for your GPU | 16 | 49.2 GB | Maximum | S85 |
| 391.6 tok/s |
| Qwen 3.5 27B | 27B | S | 169.8 tok/s |
| Qwen 3.6 27B | 27B | S | 105.9 tok/s |
| Qwen 3.5 122B A10B | 122B | S | 104.1 tok/s |
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.