Is unified memory on MacBook Pro M4 Max 128GB as fast as VRAM for Phi 4 reasoning vision 15B?

Not always. MacBook Pro M4 Max 128GB 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.

Can Phi 4 reasoning vision 15B run on MacBook Pro M4 Max 128GB?

YES — Runs Great

C45Usable

Estimated from fit model

Phi 4 reasoning vision 15B needs ~25.6 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

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) — 25.6 GB, 34.7 tok/s, Runs well

25.6 GB required92.2 GB available

28% VRAM used

Fit status

Runs well

Decode

34.7 tok/s

TTFT

5573 ms

Safe context

622K

Memory

25.6 GB / 92.2 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsPhi 4 reasoning vision 15B on MacBook Pro M4 Max 128GB
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: 34.7 tok/s decode · 5.6s TTFT (warm) · 87 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	C	Runs well	34.7 tok/s	3040 ms	622K
Coding	C	Runs well	34.7 tok/s	5573 ms	622K
Agentic Coding	C	Runs well	34.7 tok/s	8107 ms	622K
Reasoning	C	Runs well	34.7 tok/s	6587 ms	622K
RAG	C	Runs well	34.7 tok/s	10133 ms	622K

Inference speed

Phi 4 reasoning vision 15B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi 4 reasoning vision 15B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 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	131.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	83.7	Fits

Quantization options

How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	D39
Q3_K_S	3	7.4 GB	Low	D39
NVFP4	4

Get started

Copy-paste commands to run Phi 4 reasoning vision 15B on your machine.

Run

lms load hf-jamesburton--phi-4-reasoning-vision-15b-gguf && lms server start

Upgrade options

Hardware that runs Phi 4 reasoning vision 15B well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)819 GB/s (+273)

Raises estimated decode speed by about 76%.60.9 tok/s decode

Raises estimated decode speed by about 76%.

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

~$6,999 MSRP

AMD Instinct MI350X 288GBBest value

288 GB VRAM (+160)8000 GB/s (+7454)

Raises estimated decode speed by about 505%.210 tok/s decode

Raises estimated decode speed by about 505%.

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

~$8,000 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 128GB run Phi 4 reasoning vision 15B?

Yes, MacBook Pro M4 Max 128GB can run Phi 4 reasoning vision 15B with a C grade (Runs well). Expected decode speed: 34.7 tok/s.

How much VRAM does Phi 4 reasoning vision 15B need?

Phi 4 reasoning vision 15B (15B parameters) requires approximately 25.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 4 reasoning vision 15B?

The recommended quantization for Phi 4 reasoning vision 15B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 4 reasoning vision 15B run at on MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 128GB, Phi 4 reasoning vision 15B achieves approximately 34.7 tokens per second decode speed with a time-to-first-token of 5573ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 128GB run Phi 4 reasoning vision 15B for coding?

For coding workloads, Phi 4 reasoning vision 15B on MacBook Pro M4 Max 128GB receives a C grade with 34.7 tok/s and 622K context.

What context window can Phi 4 reasoning vision 15B use on MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 128GB, Phi 4 reasoning vision 15B can safely use up to 622K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 Max 128GB See all hardware for Phi 4 reasoning vision 15B

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