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

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

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

YES — Runs Great

A70Great
▲Estimated — low-sample bucket· few comparable runs

Phi 3 Mini 3.8B needs ~11.7 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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, 34.3 tok/s, Runs well
11.7 GB required17.3 GB available
68% VRAM used

Fit status

Runs well

Decode

34.3 tok/s

TTFT

5646 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 Air M4 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: 34.3 tok/s decode · 5.6s TTFT (warm) · 86 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 well34.3 tok/s3079 ms31K
CodingARuns well34.3 tok/s5646 ms31K
Agentic CodingBRuns with offload (needs ~0 GB host RAM)33.2 tok/s8494 ms31K
ReasoningARuns well34.3 tok/s6672 ms31K
RAGBRuns with offload (needs ~0 GB host RAM)33.2 tok/s10618 ms31K

Quantization options

How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on MacBook Air M4 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 Air M4 24GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen 3.5 9B		9BS15.6 tok/s
👁 Mistral
Magistral Small 2507		24BA7.3 tok/s
👁 Mistral
Devstral Small 2 24B Instruct		24BA7.3 tok/s
👁 Alibaba
Qwen 3 14B		14BS9.6 tok/s
👁 Alibaba
Qwen 3.5 4B		4BS35 tok/s

Frequently asked questions

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