Apple SiliconApple

Apple M4 Max (40c GPU, 128GB)

Name: Apple M4 Max (40c GPU, 128GB)
Brand: Apple
SKU: apple-m4-max

Curated Aggregate·2025-05-1511 workloads · 22 records

MyAI RatingNot scoredInsufficient comparison evidence

VRAM

128 GB

TDP

70 W

MSRP

$4.7k

Perf/W

0.80 tok/s/W

Cost/1K tok

$0.89/M

Tested

2025-05-15

Quick answer

How many tokens per second does Apple M4 Max (40c GPU, 128GB) produce on Llama 3 70B Q4?

Apple M4 Max (40c GPU, 128GB) has a source-attributed result of 6.0 tok/s on Llama 3 70B Q4 (batch 1, 8192-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 Silicon Pro

Apple M4 Max is an Apple Silicon SoC with configurations up to 128GB unified memory. MacBook Pro and Mac Studio use active cooling; system power and noise depend on chassis and workload.

AI Usefulness

128GB unified memory is the headline — runs 70B Q4 full-GPU without offload complexity. Expect ~35-40 tok/s on 12B models via MLX, ~8-12 tok/s on 70B Q4. The unified architecture eliminates PCIe bottlenecks and CPU-GPU transfers. Ideal for Mac-native developers, privacy-sensitive workloads, and operators who value silence + power efficiency over raw throughput. Worse $/performance than NVIDIA but better $/VRAM at the high end.

Editorial Verdict

8.5

Editor Rating

Buy for Mac-native development, privacy-sensitive workloads, and silent operation. The 128GB unified memory is transformative for large-model work. Skip if you need raw tok/s or CUDA ecosystem.

What it does well

+Up to 128GB unified memory — runs 70B Q4 full-GPU without offload complexity
+65W peak — silent, efficient, runs on battery
+Unified architecture eliminates PCIe bottlenecks and CPU-GPU transfers
+MLX framework is fast, native, and improving rapidly

Where it breaks

, ~35-40 tok/s on 12B Q4 — significantly slower than NVIDIA for raw throughput
, $3,699+ entry price — expensive for the compute performance
, macOS-only ecosystem — no CUDA, no vLLM, no TensorRT
, Non-upgradeable — you buy the RAM once and it's fixed forever

Sweet Spot

70B Q4 full-GPU at ~8-12 tok/s, 32B Q4 at ~25-30 tok/s — the best Mac for large-model inference

Bad Use Cases

×Maximum throughput on small models (any NVIDIA card beats it)
×CUDA-dependent workflows
×Budget builds (Mac mini M4 Pro is better value)
×Multi-GPU scaling (not possible on Apple Silicon)

What Breaks First

Thermal throttling on sustained inference in MacBook Pro chassis — 128GB models stay cooler due to more memory chips spreading heat. Consider Mac Studio for 24/7 operation.

Software Support

MLXllama.cpp (Metal)Ollama (Metal)LM StudioPyTorch (MPS)

macOS 15+ (excellent), Linux via Asahi (experimental), Windows (unsupported)

Best Pairings

›MLX + Llama 3.1 70B Q4 for full-GPU inference
›Ollama + Qwen 2.5 Coder 32B for coding agent
›LM Studio for GUI-based model management

Power & Cooling

65W peak, ~35W sustained decode. Negligible electricity cost. Silent operation under most loads. The most power-efficient way to run 70B models.

Verdict

Apple M4 Max (40c GPU, 128GB) with 128GB VRAM at 70W TDP, scored across 11 workloads with 22 benchmark records.

Reference workload

DeepSeek-R1 7B

48 tok/s

Quantization

Q4_K_M

8K context · batch 1

LLM Inference Performance

Benchmarks (11 workloads)

Workload	Score	Quant	Context	σ	Status	Tested
Llama 3 8B Q4 llm	56.0tok/s	Q4_K_M	8K	,	Curated Aggregate	2025-05-15
Llama 3 70B Q4 llm	8.5tok/s	Q4_K_M	8K	,	Curated Aggregate	2026-01-30
Qwen 2.5 14B llm	38.0tok/s	Q4_K_M	16K	,	Curated Aggregate	2026-03-04
SDXL image gen image	6.0img/min	FP16	,	,	Curated Aggregate	2025-01-30
Whisper transcription audio	32.0x RT	FP16	,	,	Curated Aggregate	2026-02-11
DeepSeek-R1 7B llm	48.0tok/s	Q4_K_M	8K	,	Curated Aggregate	2026-05-22
Gemma 2 9B llm	44.0tok/s	Q4_K_M	8K	,	Curated Aggregate	2025-09-11
Embedding throughput embedding	1850.0emb/s	FP16	1K	,	Curated Aggregate	2026-03-12
Llama 3 70B Q8 llm	5.2tok/s	Q8_0	4K	,	Curated Aggregate	2026-02-08
Mistral 7B llm	64.0tok/s	Q4_K_M	4K	,	Curated Aggregate	2024-12-08
Asure 12B llm	35.0tok/s	Q4_K_M	8K	,	Curated Aggregate	2025-05-01

MyAI Score: not scored

There is insufficient comparable evidence to score Apple M4 Max (40c GPU, 128GB). A score needs results for multiple devices with matching workload, runtime version, quantization, context and batch size.

Workload Fit

Planning estimates for 128GB: 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

Won't fit

Reported batch-one examples

DeepSeek-R1 7B48 tok/s

Embedding throughput1850 emb/s

Qwen 2.5 14B38 tok/s

Source

Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — llama.cpp Metal.

View source How we benchmark →

Public Trust Layer

Trust score

6/10

MyAI rating

Not scored

Runs

Not documented

Freshness

Aging

Source-linked row with explicit verification status.

Record date: 2026-05-22; 110 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 $4.7k 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.