willitrun·ai

Can Phi 3 Medium 14B run on Mac mini M4 64GB?

YES — Runs Great

C55Usable
Estimated — low-sample bucket· few comparable runs

Phi 3 Medium 14B needs ~19.4 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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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) — 19.4 GB, 9.6 tok/s, Runs well
19.4 GB required46.1 GB available
42% VRAM used

Fit status

Runs well

Decode

9.6 tok/s

TTFT

20228 ms

Safe context

128K

Memory

19.4 GB / 46.1 GB

Memory breakdown

Weights8.5 GB
KV Cache3.1 GB
Runtime0.9 GB
Headroom6.9 GB

See how fast it feels

See how fast it feelsPhi 3 Medium 14B on Mac mini M4 64GB
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: 9.6 tok/s decode · 20.2s TTFT (warm) · 24 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
ChatCRuns well9.6 tok/s11034 ms128K
CodingCRuns well9.6 tok/s20228 ms128K
Agentic CodingBRuns well9.6 tok/s29423 ms128K
ReasoningCRuns well9.6 tok/s23906 ms128K
RAGBRuns well9.6 tok/s36779 ms128K

Inference speed

Phi 3 Medium 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi 3 Medium 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~151 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M151.1Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M96.4Fits
RX 7900 XTX 24GB
24 GBQ4_K_M87.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M82.5Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M80.7Tight
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M70.1Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M58.4Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M55.4Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M38.1Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M38.1Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M30.2Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M28.4Heavy offload
MacBook Pro M1 Max 64GB
64 GBQ4_K_M27.7Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M23.3Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M16.7Heavy offload
NVIDIARTX 4060 8GB
8 GBQ4_K_M6.1Too big

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 3 Medium 14B (14B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC53
Q3_K_S
3
6.9 GB
LowC53
NVFP4
4
7.8 GB
MediumC53
Q4_K_M
4
8.5 GB
MediumC54
Q5_K_M
5
10.1 GB
HighC54
Q6_K
6
11.5 GB
HighC55
Q8_0
8
15.0 GB
Very HighB56
F16Best for your GPU
16
28.7 GB
MaximumB59

Get started

Copy-paste commands to run Phi 3 Medium 14B on your machine.

Run

ollama run phi3:medium

Upgrade-Optionen

Hardware, die Phi 3 Medium 14B gut ausführt

MacBook Pro M4 Max 96GBGünstige Wahl
96 GB Unified (+32)546 GB/s (+426)
B
Raises estimated decode speed by about 297%.38.1 tok/s Dekodierung

Raises estimated decode speed by about 297%.

Adds memory headroom for longer context windows and future model growth.

ca. $2,499 MSRP

MacBook Pro M3 Max 128GBBestes Preis-Leistungs-Verhältnis
128 GB Unified (+64)400 GB/s (+280)
B
Raises estimated decode speed by about 215%.30.2 tok/s Dekodierung

Raises estimated decode speed by about 215%.

Adds memory headroom for longer context windows and future model growth.

ca. $2,499 MSRP

Mac Studio M3 Ultra 96GBApple-Upgrade
96 GB Unified (+32)819 GB/s (+699)
B
Raises estimated decode speed by about 630%.70.1 tok/s Dekodierung

Raises estimated decode speed by about 630%.

Adds memory headroom for longer context windows and future model growth.

ca. $3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run Phi 3 Medium 14B?

Yes, Mac mini M4 64GB can run Phi 3 Medium 14B with a C grade (Runs well). Expected decode speed: 9.6 tok/s.

How much VRAM does Phi 3 Medium 14B need?

Phi 3 Medium 14B (14B parameters) requires approximately 19.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 3 Medium 14B?

The recommended quantization for Phi 3 Medium 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3 Medium 14B run at on Mac mini M4 64GB?

On Mac mini M4 64GB, Phi 3 Medium 14B achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20228ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Phi 3 Medium 14B for coding?

For coding workloads, Phi 3 Medium 14B on Mac mini M4 64GB receives a C grade with 9.6 tok/s and 128K context.

What context window can Phi 3 Medium 14B use on Mac mini M4 64GB?

On Mac mini M4 64GB, Phi 3 Medium 14B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for Phi 3 Medium 14B?

Not always. Mac mini M4 64GB 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 M4 64GBSee all hardware for Phi 3 Medium 14B
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