Qwen 2.5 Math 7B needs ~7.2 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~42 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
41.9 tok/s
TTFT
4625 ms
Safe context
4K
Memory
7.2 GB / 12.0 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 | 41.9 tok/s | 2523 ms | 4K |
| Coding | B | Runs well | 41.9 tok/s | 4625 ms | 4K |
| Agentic Coding | B | Runs well | 41.9 tok/s | 6727 ms | 4K |
| Reasoning | B | Runs well | 41.9 tok/s | 5466 ms | 4K |
| RAG | B | Runs well | 41.9 tok/s | 8409 ms | 4K |
How Qwen 2.5 Math 7B (7B params) fits at each quantization level on Intel Arc A730M 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_S | 3 | 3.4 GB | Low | C54 |
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 99Yes, Intel Arc A730M 12GB can run Qwen 2.5 Math 7B with a B grade (Runs well). Expected decode speed: 41.9 tok/s.
Qwen 2.5 Math 7B (7B parameters) requires approximately 7.2 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 A730M 12GB, Qwen 2.5 Math 7B achieves approximately 41.9 tokens per second decode speed with a time-to-first-token of 4625ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 Math 7B on Intel Arc A730M 12GB receives a B grade with 41.9 tok/s and 4K context.
On Intel Arc A730M 12GB, 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-a730m-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| C55 |
Q4_K_M | 4 | 4.3 GB | Medium | B55 |
Q5_K_M | 5 | 5.0 GB | High | B56 |
Q6_K | 6 | 5.7 GB | High | B57 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B56 |
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.