Can Phi-4 Mini Reasoning 4B run on Intel Arc B580 12GB?

YES — Runs Great

S88Excellent

Estimated from fit model

Phi-4 Mini Reasoning 4B needs ~5.9 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 5.9 GB, 53.2 tok/s, Runs well

5.9 GB required12.0 GB available

49% VRAM used

Fit status

Runs well

Decode

53.2 tok/s

TTFT

3639 ms

Safe context

83K

Memory

5.9 GB / 12.0 GB

Memory breakdown

Weights2.3 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsPhi-4 Mini Reasoning 4B on Intel Arc B580 12GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 53.2 tok/s decode · 3.6s TTFT (warm) · 133 tok/s prefill

What limits this setup

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.

Best improvement path

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	53.2 tok/s	1985 ms	83K
Coding	S	Runs well	53.2 tok/s	3639 ms	83K
Agentic Coding	S	Runs well	53.2 tok/s	5293 ms	83K
Reasoning	S	Runs well	53.2 tok/s	4301 ms	83K
RAG	S	Runs well	53.2 tok/s	6617 ms	83K

Inference speed

Phi-4 Mini Reasoning 4B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi-4 Mini Reasoning 4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~72 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	72.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	60.8	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	60.8	Fits
RTX 4060 8GB	8 GB	Q4_K_M	60.8	Fits
RTX 3090 24GB	24 GB	Q4_K_M	53.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	53.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	53.2	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	53.2	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	53.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	53.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	53.2	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	A85
Q3_K_S	3	1.9 GB	Low	S85
NVFP4	4	2.1 GB	Medium	S85
Q4_K_M	4	2.3 GB	Medium	S86
Q5_K_M	5	2.7 GB	High	S86
Q6_K	6	3.1 GB	High	S87
Q8_0	8	4.1 GB	Very High	S88
F16Best for your GPU	16	7.8 GB	Maximum	S89

Get started

Copy-paste commands to run Phi-4 Mini Reasoning 4B on your machine.

Run

ollama run phi4-mini

Your hardware

More models your Intel Arc B580 12GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	42.9 tok/s
Qwen 3 14B	14B	A	17.8 tok/s
Qwen 3.5 4B	4B	S	56 tok/s
Qwen 3 8B	8B	S	48.2 tok/s
Phi-4-reasoning-plus 14B	14.7B	A	14.4 tok/s

Frequently asked questions

Can Intel Arc B580 12GB run Phi-4 Mini Reasoning 4B?

Yes, Intel Arc B580 12GB can run Phi-4 Mini Reasoning 4B with a S grade (Runs well). Expected decode speed: 53.2 tok/s.

How much VRAM does Phi-4 Mini Reasoning 4B need?

Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 5.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi-4 Mini Reasoning 4B?

The recommended quantization for Phi-4 Mini Reasoning 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi-4 Mini Reasoning 4B run at on Intel Arc B580 12GB?

On Intel Arc B580 12GB, Phi-4 Mini Reasoning 4B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.

Can Intel Arc B580 12GB run Phi-4 Mini Reasoning 4B for coding?

For coding workloads, Phi-4 Mini Reasoning 4B on Intel Arc B580 12GB receives a S grade with 53.2 tok/s and 83K context.

What context window can Phi-4 Mini Reasoning 4B use on Intel Arc B580 12GB?

On Intel Arc B580 12GB, Phi-4 Mini Reasoning 4B can safely use up to 83K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Phi-4 Mini Reasoning 4B feels slow on Intel Arc B580 12GB?

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.

Would CUDA be a better path than Intel Arc B580 12GB for Phi-4 Mini Reasoning 4B?

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.

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