Phi 4 Mini 4B needs ~6.0 GB VRAM. Intel Arc B580 12GB has 12.0 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
Runs well
Decode
56.0 tok/s
TTFT
3457 ms
Safe context
81K
Memory
6.0 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 | A | Runs well | 56.0 tok/s | 1886 ms | 81K |
| Coding | A | Runs well | 56.0 tok/s | 3457 ms | 81K |
| Agentic Coding | A | Runs well | 56.0 tok/s | 5029 ms | 81K |
| Reasoning | A | Runs well | 56.0 tok/s | 4086 ms | 81K |
| RAG | A | Runs well | 56.0 tok/s | 6286 ms | 81K |
How Phi 4 Mini 4B (4B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B68 |
Q3_K_S | 3 | 2.0 GB | Low | B69 |
NVFP4 | 4 |
Copy-paste commands to run Phi 4 Mini 4B on your machine.
Run
ollama run phi4-miniYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 42.9 tok/s | |
| Qwen 3 14B | 14B | A | 17.8 tok/s |
Yes, Intel Arc B580 12GB can run Phi 4 Mini 4B with a A grade (Runs well). Expected decode speed: 56.0 tok/s.
Phi 4 Mini 4B (4B parameters) requires approximately 6.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 4 Mini 4B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc B580 12GB, Phi 4 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 4 Mini 4B on Intel Arc B580 12GB receives a A grade with 56.0 tok/s and 81K context.
On Intel Arc B580 12GB, Phi 4 Mini 4B can safely use up to 81K 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-4-mini-4b-on-arc-b580-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
2.2 GB |
| Medium |
| B69 |
Q4_K_M | 4 | 2.4 GB | Medium | B69 |
Q5_K_M | 5 | 2.9 GB | High | B70 |
Q6_K | 6 | 3.3 GB | High | A70 |
Q8_0 | 8 | 4.3 GB | Very High | A72 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | A72 |
| Qwen 3 8B | 8B | S | 48.2 tok/s |
 Phi-4-reasoning-plus 14B | 14.7B | A | 14.4 tok/s |
 Nemotron Nano 8B | 8B | S | 48.2 tok/s |
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.