Intel Arc B570 10GBBudget pick10 GB VRAM (+2)
B
Adds memory headroom for longer context windows and future model growth.52.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$219 MSRP
Qwen 2.5 Math 7B needs ~6.8 GB VRAM. Intel Arc A750 8GB has 8.0 GB. With Q4_K_M quantization, expect ~56 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
Tight fit
Decode
56.0 tok/s
TTFT
3458 ms
Safe context
4K
Memory
6.8 GB / 8.0 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 | B | Runs well | 56.0 tok/s | 1886 ms | 4K |
| Coding | B | Tight fit | 56.0 tok/s | 3458 ms | 4K |
| Agentic Coding | B | Runs with offload | 56.0 tok/s | 5030 ms | 4K |
| Reasoning | B | Tight fit | 56.0 tok/s | 4087 ms | 4K |
| RAG | B | Runs with offload | 56.0 tok/s | 6288 ms | 4K |
How Qwen 2.5 Math 7B (7B params) fits at each quantization level on Intel Arc A750 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B57 |
Q3_K_S | 3 | 3.4 GB | Low | B58 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 2.5 Math 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "Qwen/Qwen2.5-Math-7B-Instruct" \
--hf-file "Qwen2.5-Math-7B-Instruct-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Intel Arc B570 10GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$219 MSRP
Intel Arc B580 12GBBest valueAdds memory headroom for longer context windows and future model growth.
~$249 MSRP
Intel Arc Pro A60 12GBIntel upgradeAdds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, Intel Arc A750 8GB can run Qwen 2.5 Math 7B with a B grade (Tight fit). Expected decode speed: 56.0 tok/s.
Qwen 2.5 Math 7B (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 2.5 Math 7B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A750 8GB, Qwen 2.5 Math 7B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3458ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 Math 7B on Intel Arc A750 8GB receives a B grade with 56.0 tok/s and 4K context.
On Intel Arc A750 8GB, Qwen 2.5 Math 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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/qwen-2.5-math-7b-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 |
| B58 |
Q4_K_M | 4 | 4.3 GB | Medium | B57 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | B57 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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.