Can Phi-4 Mini Reasoning 4B run on Mac mini M2 24GB?

YES — Runs Great

A84Great

Estimated from fit model

Phi-4 Mini Reasoning 4B needs ~7.3 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 7.3 GB, 30.1 tok/s, Runs well

7.3 GB required17.3 GB available

42% VRAM used

Fit status

Runs well

Decode

30.1 tok/s

TTFT

6422 ms

Safe context

125K

Memory

7.3 GB / 17.3 GB

Memory breakdown

Weights2.3 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsPhi-4 Mini Reasoning 4B on Mac mini M2 24GB
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: 30.1 tok/s decode · 6.4s TTFT (warm) · 75 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	30.1 tok/s	3503 ms	125K
Coding	A	Runs well	30.1 tok/s	6422 ms	125K
Agentic Coding	S	Runs well	30.1 tok/s	9342 ms	125K
Reasoning	A	Runs well	30.1 tok/s	7590 ms	125K
RAG	S	Runs well	30.1 tok/s	11677 ms	125K

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 Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	A83
Q3_K_S	3	1.9 GB	Low	A83
NVFP4	4	2.1 GB	Medium	A83
Q4_K_M	4	2.3 GB	Medium	A83
Q5_K_M	5	2.7 GB	High	A83
Q6_K	6	3.1 GB	High	A84
Q8_0	8	4.1 GB	Very High	A84
F16Best for your GPU	16	7.8 GB	Maximum	S88

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 Mac mini M2 24GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	12.7 tok/s
Magistral Small 2507	24B	B	3.7 tok/s
Devstral Small 2 24B Instruct	24B	B	3.7 tok/s
Qwen 3 14B	14B	S	8.2 tok/s
Qwen 3.5 4B	4B	S	28.6 tok/s

Frequently asked questions

Can Mac mini M2 24GB run Phi-4 Mini Reasoning 4B?

Yes, Mac mini M2 24GB can run Phi-4 Mini Reasoning 4B with a A grade (Runs well). Expected decode speed: 30.1 tok/s.

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

Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 7.3 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 Mac mini M2 24GB?

On Mac mini M2 24GB, Phi-4 Mini Reasoning 4B achieves approximately 30.1 tokens per second decode speed with a time-to-first-token of 6422ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Phi-4 Mini Reasoning 4B for coding?

For coding workloads, Phi-4 Mini Reasoning 4B on Mac mini M2 24GB receives a A grade with 30.1 tok/s and 125K context.

What context window can Phi-4 Mini Reasoning 4B use on Mac mini M2 24GB?

On Mac mini M2 24GB, Phi-4 Mini Reasoning 4B can safely use up to 125K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M2 24GB as fast as VRAM for Phi-4 Mini Reasoning 4B?

Not always. Mac mini M2 24GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac mini M2 24GB See all hardware for Phi-4 Mini Reasoning 4B

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