Phi-4 Mini Reasoning 4B needs ~5.3 GB VRAM. Intel Arc A380 6GB has 6.0 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
Tight fit
Decode
42.3 tok/s
TTFT
4580 ms
Safe context
24K
Memory
5.3 GB / 6.0 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 | S | Runs well | 42.3 tok/s | 2498 ms | 24K |
| Coding | S | Tight fit | 42.3 tok/s | 4580 ms | 24K |
| Agentic Coding | A | Very compromised (needs ~0.3 GB host RAM) | 24.8 tok/s | 11374 ms | 24K |
| Reasoning | S | Tight fit | 42.3 tok/s | 5413 ms | 24K |
| RAG | A | Very compromised (needs ~0.3 GB host RAM) | 24.8 tok/s | 14218 ms | 24K |
How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on Intel Arc A380 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | S91 |
Q3_K_S | 3 | 1.9 GB | Low | S92 |
NVFP4 | 4 | 2.1 GB | Medium | S92 |
Q4_K_M | 4 | 2.3 GB | Medium | S91 |
Q5_K_M | 5 | 2.7 GB | High | S91 |
Q6_KBest for your GPU | 6 | 3.1 GB | High | S91 |
Q8_0 | 8 | 4.1 GB | Very High | F0 |
F16 | 16 | 7.8 GB | Maximum | F0 |
Copy-paste commands to run Phi-4 Mini Reasoning 4B on your machine.
Run
ollama run phi4-miniYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 4B | 4B | S | 28.7 tok/s |
Yes, Intel Arc A380 6GB can run Phi-4 Mini Reasoning 4B with a S grade (Tight fit). Expected decode speed: 42.3 tok/s.
Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 5.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4 Mini Reasoning 4B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A380 6GB, Phi-4 Mini Reasoning 4B achieves approximately 42.3 tokens per second decode speed with a time-to-first-token of 4580ms using Q4_K_M quantization.
For coding workloads, Phi-4 Mini Reasoning 4B on Intel Arc A380 6GB receives a S grade with 42.3 tok/s and 24K context.
On Intel Arc A380 6GB, Phi-4 Mini Reasoning 4B can safely use up to 24K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-4-mini-reasoning-on-arc-a380-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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