Will It Run AI

Can Phi 4 reasoning vision 15B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

C45Usable
Estimated from fit model

Phi 4 reasoning vision 15B needs ~22.2 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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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) 22.2 GB, 25.4 tok/s, Runs well
22.2 GB required69.1 GB available
32% VRAM used

Fit status

Runs well

Decode

25.4 tok/s

TTFT

7636 ms

Safe context

443K

Memory

22.2 GB / 69.1 GB

Memory breakdown

Weights9.2 GB
KV Cache1.8 GB
Runtime0.9 GB
Headroom10.4 GB

See how fast it feels

See how fast it feelsPhi 4 reasoning vision 15B on MacBook Pro M2 Max 96GB
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: 25.4 tok/s decode · 7.6s TTFT (warm) · 63 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 well25.4 tok/s4165 ms443K
CodingCRuns well25.4 tok/s7636 ms443K
Agentic CodingCRuns well25.4 tok/s11106 ms443K
ReasoningCRuns well25.4 tok/s9024 ms443K
RAGCRuns well25.4 tok/s13883 ms443K

Quantization options

How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.9 GB
LowC40
Q3_K_S
3
7.4 GB
LowC40
NVFP4
4
8.4 GB
MediumC41
Q4_K_M
4
9.2 GB
MediumC41
Q5_K_M
5
10.8 GB
HighC41
Q6_K
6
12.3 GB
HighC41
Q8_0
8
16.1 GB
Very HighC42
F16Best for your GPU
16
30.7 GB
MaximumC45

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

Opciones de mejora

Hardware que ejecuta bien Phi 4 reasoning vision 15B

Frequently asked questions

Can MacBook Pro M2 Max 96GB run Phi 4 reasoning vision 15B?

Yes, MacBook Pro M2 Max 96GB can run Phi 4 reasoning vision 15B with a C grade (Runs well). Expected decode speed: 25.4 tok/s.

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

Phi 4 reasoning vision 15B (15B parameters) requires approximately 22.2 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 M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Phi 4 reasoning vision 15B achieves approximately 25.4 tokens per second decode speed with a time-to-first-token of 7636ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run Phi 4 reasoning vision 15B for coding?

For coding workloads, Phi 4 reasoning vision 15B on MacBook Pro M2 Max 96GB receives a C grade with 25.4 tok/s and 443K context.

What context window can Phi 4 reasoning vision 15B use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Phi 4 reasoning vision 15B can safely use up to 443K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for Phi 4 reasoning vision 15B?

Not always. MacBook Pro M2 Max 96GB 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 MacBook Pro M2 Max 96GBSee all hardware for Phi 4 reasoning vision 15B
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