Magistral Small 2507 needs ~18.8 GB but Intel Arc A750 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
10.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.4 tok/s
TTFT
79838 ms
Safe context
4K
Memory
18.8 GB / 8.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 18.8 GB, but this setup only exposes 8.0 GB of usable VRAM.
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.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
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 | F | Too heavy | 2.4 tok/s | 43548 ms | 4K |
| Coding | F | Too heavy | 2.4 tok/s | 79838 ms | 4K |
| Agentic Coding | F | Too heavy | 2.4 tok/s | 116128 ms | 4K |
| Reasoning | F | Too heavy | 2.4 tok/s | 94354 ms | 4K |
| RAG | F | Too heavy | 2.4 tok/s | 145160 ms | 4K |
How Magistral Small 2507 (24B params) fits at each quantization level on Intel Arc A750 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | F0 |
Q3_K_S | 3 | 11.8 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
Intel Arc Pro B60 24GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$599 MSRP
RTX 5090 32GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,999 MSRP
Intel Data Center GPU Max 1550 128GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$15,000 MSRP
Gaudi 3 128GBIntel upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$15,000 MSRP
No, Magistral Small 2507 requires more memory than Intel Arc A750 8GB provides.
Magistral Small 2507 (24B parameters) requires approximately 18.8 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 Intel Arc A750 8GB, Magistral Small 2507 achieves approximately 2.4 tokens per second decode speed with a time-to-first-token of 79838ms using Q4_K_M quantization.
For coding workloads, Magistral Small 2507 on Intel Arc A750 8GB receives a F grade with 2.4 tok/s and 4K context.
On Intel Arc A750 8GB, Magistral Small 2507 can safely use up to 4K 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-arc-a750-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 14.6 GB | Medium | F0 |
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 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
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.
