Qwen 3 8B needs ~8.8 GB VRAM. Intel Arc A750 8GB has 8.0 GB. With Q4_K_M quantization, expect ~30 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
0.8 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.4 GB host RAM)
Decode
29.9 tok/s
TTFT
6470 ms
Safe context
10K
Memory
8.8 GB / 8.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs with offload | 48.5 tok/s | 2177 ms | 10K |
| Coding | A | Very compromised (needs ~0.4 GB host RAM) | 29.9 tok/s | 6470 ms | 10K |
| Agentic Coding | F | Too heavy | 18.7 tok/s | 15061 ms | 10K |
| Reasoning | A | Very compromised (needs ~0.4 GB host RAM) | 29.9 tok/s | 7646 ms | 10K |
| RAG | F | Too heavy | 18.7 tok/s | 18826 ms |
How Qwen 3 8B (8B params) fits at each quantization level on Intel Arc A750 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | S94 |
Q3_K_S | 3 | 3.9 GB | Low | S93 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 3 8B on your machine.
Run
ollama run qwen3:8bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | A | 23.1 tok/s |
Yes, Intel Arc A750 8GB can run Qwen 3 8B with a A grade (Very compromised (needs ~0.4 GB host RAM)). Expected decode speed: 29.9 tok/s.
Qwen 3 8B (8B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3 8B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A750 8GB, Qwen 3 8B achieves approximately 29.9 tokens per second decode speed with a time-to-first-token of 6470ms using Q4_K_M quantization.
For coding workloads, Qwen 3 8B on Intel Arc A750 8GB receives a A grade with 29.9 tok/s and 10K context.
On Intel Arc A750 8GB, Qwen 3 8B can safely use up to 10K 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/qwen-3-8b-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:
| 10K |
| Medium |
| S93 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | S93 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.