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URL: https://willitrunai.com/can-run/phi-3-mini-3.8b-on-m4-pro-24gb

⇱ Phi 3 Mini 3.8B on MacBook Pro M4 Pro 24GB? YES

Can Phi 3 Mini 3.8B run on MacBook Pro M4 Pro 24GB?

YES — Runs Great

A72Great
▲Estimated — low-sample bucket· few comparable runs

Phi 3 Mini 3.8B needs ~11.7 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Share:

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, 53.2 tok/s, Runs well
11.7 GB required17.3 GB available
68% VRAM used

Fit status

Runs well

Decode

53.2 tok/s

TTFT

3639 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 MacBook Pro M4 Pro 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: 53.2 tok/s decode · 3.6s TTFT (warm) · 133 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well53.2 tok/s1985 ms31K
CodingARuns well53.2 tok/s3639 ms31K
Agentic CodingBRuns with offload (needs ~0 GB host RAM)53.2 tok/s5293 ms31K
ReasoningARuns well53.2 tok/s4301 ms31K
RAGBRuns with offload (needs ~0 GB host RAM)53.2 tok/s6617 ms31K

Quantization options

How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
1.5 GB
LowB62
Q3_K_S
3
1.9 GB
LowB63
NVFP4
4
2.1 GB
MediumB63
Q4_K_M
4
2.3 GB
MediumB63
Q5_K_M
5
2.7 GB
HighB63
Q6_K
6
3.1 GB
HighB64
Q8_0
8
4.1 GB
Very HighB64
F16Best for your GPU
16
7.8 GB
MaximumB68

Get started

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

Run

ollama run phi3:mini

Your hardware

More models your MacBook Pro M4 Pro 24GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen 3.5 9B		9BS37.9 tok/s
👁 Mistral
Magistral Small 2507		24BA17.8 tok/s
👁 Mistral
Devstral Small 2 24B Instruct		24BA17.8 tok/s
👁 Alibaba
Qwen 3 14B		14BS23.4 tok/s
👁 Alibaba
Qwen 3.5 4B		4BS56 tok/s

Frequently asked questions

See all results for MacBook Pro M4 Pro 24GBSee all hardware for Phi 3 Mini 3.8B