Intel Arc B570 10GBBudget pick10 GB VRAM (+2)
C
Adds memory headroom for longer context windows and future model growth.48.1 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$219 MSRP
OpenChat 3.5 7B Qwen v2.0 i1 needs ~6.8 GB VRAM. Intel Arc A580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~59 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
58.8 tok/s
TTFT
3295 ms
Safe context
40K
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 | C | Runs well | 58.8 tok/s | 1797 ms | 40K |
| Coding | C | Tight fit | 58.8 tok/s | 3295 ms | 40K |
| Agentic Coding | C | Runs with offload | 58.8 tok/s | 4793 ms | 40K |
| Reasoning | C | Tight fit | 58.8 tok/s | 3894 ms | 40K |
| RAG | C | Runs with offload | 58.8 tok/s | 5991 ms | 40K |
How OpenChat 3.5 7B Qwen v2.0 i1 (7B params) fits at each quantization level on Intel Arc A580 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 |
Copy-paste commands to run OpenChat 3.5 7B Qwen v2.0 i1 on your machine.
Run
lms load hf-mradermacher--openchat-3-5-7b-qwen-v2-0-i1-gguf && lms server startUpgrade 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
Yes, Intel Arc A580 8GB can run OpenChat 3.5 7B Qwen v2.0 i1 with a C grade (Tight fit). Expected decode speed: 58.8 tok/s.
OpenChat 3.5 7B Qwen v2.0 i1 (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.
The recommended quantization for OpenChat 3.5 7B Qwen v2.0 i1 is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A580 8GB, OpenChat 3.5 7B Qwen v2.0 i1 achieves approximately 58.8 tokens per second decode speed with a time-to-first-token of 3295ms using Q4_K_M quantization.
For coding workloads, OpenChat 3.5 7B Qwen v2.0 i1 on Intel Arc A580 8GB receives a C grade with 58.8 tok/s and 40K context.
On Intel Arc A580 8GB, OpenChat 3.5 7B Qwen v2.0 i1 can safely use up to 40K tokens of context. The model's official context limit is —, 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/hf-mradermacher--openchat-3-5-7b-qwen-v2-0-i1-gguf-on-arc-a580-8gb" 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 |
| C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C52 |
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.