Can Phi 3 Mini 3.8B run on Mac mini M2 24GB?

YES — Runs Great

B70Good

Estimated from fit model

Phi 3 Mini 3.8B needs ~11.7 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~28 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) — 11.7 GB, 28.0 tok/s, Runs well

11.7 GB required17.3 GB available

68% VRAM used

Fit status

Runs well

Decode

28.0 tok/s

TTFT

6904 ms

Safe context

31K

Memory

11.7 GB / 17.3 GB

Memory breakdown

Weights2.3 GB

KV Cache5.9 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsPhi 3 Mini 3.8B on Mac mini M2 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 28.0 tok/s decode · 6.9s TTFT (warm) · 70 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	B	Runs well	28.0 tok/s	3766 ms	31K
Coding	B	Runs well	28.0 tok/s	6904 ms	31K
Agentic Coding	B	Runs with offload (needs ~0 GB host RAM)	27.1 tok/s	10388 ms	31K
Reasoning	B	Runs well	28.0 tok/s	8159 ms	31K
RAG	B	Runs with offload (needs ~0 GB host RAM)	27.1 tok/s	12984 ms	31K

Quantization options

How Phi 3 Mini 3.8B (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	B62
Q3_K_S	3	1.9 GB	Low	B63
NVFP4	4	2.1 GB	Medium	B63
Q4_K_M	4	2.3 GB	Medium	B63
Q5_K_M	5	2.7 GB	High	B63
Q6_K	6	3.1 GB	High	B64
Q8_0	8	4.1 GB	Very High	B64
F16Best for your GPU	16	7.8 GB	Maximum	B68

Get started

Copy-paste commands to run Phi 3 Mini 3.8B on your machine.

Run

ollama run phi3:mini

Frequently asked questions

Can Mac mini M2 24GB run Phi 3 Mini 3.8B?

Yes, Mac mini M2 24GB can run Phi 3 Mini 3.8B with a B grade (Runs well). Expected decode speed: 28.0 tok/s.

How much VRAM does Phi 3 Mini 3.8B need?

Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 11.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 3 Mini 3.8B?

The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3 Mini 3.8B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, Phi 3 Mini 3.8B achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6904ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on Mac mini M2 24GB receives a B grade with 28.0 tok/s and 31K context.

What context window can Phi 3 Mini 3.8B use on Mac mini M2 24GB?

On Mac mini M2 24GB, Phi 3 Mini 3.8B can safely use up to 31K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M2 24GB as fast as VRAM for Phi 3 Mini 3.8B?

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 3 Mini 3.8B

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