Intel Arc A770 16GBBudget pick16 GB VRAM (+6)560 GB/s (+180)
A
Adds memory headroom for longer context windows and future model growth.56 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
Phi 3.5 Mini 4B needs ~10.2 GB VRAM. Intel Arc B570 10GB has 10.0 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
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0 GB host RAM)
Decode
56.0 tok/s
TTFT
3457 ms
Safe context
15K
Memory
10.2 GB / 10.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
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.
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 | A | Runs well | 56.0 tok/s | 1886 ms | 15K |
| Coding | B | Runs with offload (needs ~0 GB host RAM) | 56.0 tok/s | 3457 ms | 15K |
| Agentic Coding | F | Too heavy | 24.5 tok/s | 11508 ms | 15K |
| Reasoning | B | Runs with offload (needs ~0 GB host RAM) | 56.0 tok/s | 4086 ms | 15K |
| RAG | F | Too heavy | 24.5 tok/s | 14385 ms |
How Phi 3.5 Mini 4B (4B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B65 |
Q3_K_S | 3 | 2.0 GB | Low | B65 |
NVFP4 | 4 |
Copy-paste commands to run Phi 3.5 Mini 4B on your machine.
Run
ollama run phi3.5Upgrade options
Intel Arc A770 16GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$349 MSRP
Intel Arc Pro B50 16GBBest valueAdds memory headroom for longer context windows and future model growth.
~$399 MSRP
Yes, Intel Arc B570 10GB can run Phi 3.5 Mini 4B with a B grade (Runs with offload (needs ~0 GB host RAM)). Expected decode speed: 56.0 tok/s.
Phi 3.5 Mini 4B (4B parameters) requires approximately 10.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3.5 Mini 4B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc B570 10GB, Phi 3.5 Mini 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
For coding workloads, Phi 3.5 Mini 4B on Intel Arc B570 10GB receives a B grade with 56.0 tok/s and 15K context.
On Intel Arc B570 10GB, Phi 3.5 Mini 4B can safely use up to 15K tokens of context. The model's official context limit is 128K, 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/phi-3.5-mini-4b-on-arc-b570-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 15K |
2.2 GB |
| Medium |
| B66 |
Q4_K_M | 4 | 2.4 GB | Medium | B66 |
Q5_K_M | 5 | 2.9 GB | High | B67 |
Q6_K | 6 | 3.3 GB | High | B67 |
Q8_0Best for your GPU | 8 | 4.3 GB | Very High | B69 |
F16 | 16 | 8.2 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.