Apple SiliconApple

Apple M2 Max (38c GPU, 96GB)

Name: Apple M2 Max (38c GPU, 96GB)
Brand: Apple
SKU: apple-m2-max

Curated Aggregate·2025-10-123 workloads · 4 records

MyAI RatingNot scoredInsufficient comparison evidence

VRAM

96 GB

TDP

60 W

MSRP

$3.3k

Perf/W

0.63 tok/s/W

Cost/1K tok

$0.92/M

Tested

2025-10-12

Quick answer

How fast is Apple M2 Max (38c GPU, 96GB) for local AI workloads?

Apple M2 Max (38c GPU, 96GB) has a source-attributed result of 38.0 tok/s on Llama 3 8B Q4 (batch 1, 4096-token context, Q4_K_M; runtime not documented). This is a reference report, not an independently verified lab result. A 70B Q4_K_M artifact needs roughly 40GB for weights alone; smaller memory configurations require explicit offload or model splitting and do not establish full-GPU residency.

Overview

Apple Value

Apple M2 Max — previous-generation Apple Silicon with up to 96GB unified memory, 38-core GPU, 16-core Neural Engine. Fabricated on TSMC 5nm. Available in MacBook Pro and Mac Studio.

AI Usefulness

96GB unified memory runs 70B Q4 full-GPU at ~20-25 tok/s via MLX. Previous-generation but still competitive for memory-capacity-bound workloads. The 96GB ceiling at lower cost than M3/M4 Max makes this a value play for large-model inference on Apple Silicon. Best for: budget-conscious Mac AI developers and anyone who needs >64GB unified memory without M3 Ultra pricing.

Verdict

Apple M2 Max (38c GPU, 96GB) with 96GB VRAM at 60W TDP, scored across 3 workloads with 4 benchmark records.

Reference workload

Llama 3 8B Q4

38 tok/s

Quantization

Q4_K_M

4K context · batch 1

LLM Inference Performance

Benchmarks (3 workloads)

Workload	Score	Quant	Context	σ	Status	Tested
Llama 3 8B Q4 llm	38.0tok/s	Q4_K_M	4K	,	Curated Aggregate	2025-10-12
Mistral 7B llm	42.0tok/s	Q4_K_M	4K	,	Curated Aggregate	2024-12-30
Gemma 2 9B llm	32.0tok/s	Q4_K_M	8K	,	Curated Aggregate	2024-07-23

MyAI Score: not scored

There is insufficient comparable evidence to score Apple M2 Max (38c GPU, 96GB). A score needs results for multiple devices with matching workload, runtime version, quantization, context and batch size.

Workload Fit

Planning estimates for 96GB: weights plus at least 2GB or 10% overhead. Actual KV cache depends on model, context and cache format; confirm the artifact before buying.

FP16

7-8B

Excellent

13-14B

Excellent

32B

Excellent

70B

Excellent

Good

Won't fit

Reported batch-one examples

Llama 3 8B Q438 tok/s

Mistral 7B42 tok/s

Gemma 2 9B32 tok/s

Source

Community-submitted — llama.cpp Metal.

View source How we benchmark →

Public Trust Layer

Trust score

6/10

MyAI rating

Not scored

Runs

Not documented

Freshness

Stale

Source-linked row with explicit verification status.

Record date: 2025-10-12; 332 days old.

Open primary source

Best place to start

Where to buy

Search first

Retailer we'd check first

Amazon search

Amazon is a fast way to check live availability, but memory tier and exact SKU matter more than the first visible price.

Search Amazon listings

+Cross-check the current ask against $3.3k and any reputable open-box or used options.
+Verify RAM, storage, and chip binning carefully. Small spec differences change AI usability dramatically.
+If you plan long 24/7 inference runs, warranty and thermals matter more than a tiny discount.

There is insufficient comparable evidence for a purchase recommendation. Check model fit, software support and a current seller quote.

Affiliate note: we do not have a device-level ASIN yet, so this opens tagged Amazon search results for the exact product name.