Can Apertus v1.5 70B run on MacBook Pro M4 Max 96GB?

YES — Tight Fit

A75Great

Estimated from fit model

Apertus v1.5 70B needs ~60.1 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Memory bandwidth

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) — 60.1 GB, 14.9 tok/s, Tight fit

60.1 GB required69.1 GB available

87% VRAM used

Fit status

Tight fit

Decode

14.9 tok/s

TTFT

13019 ms

Safe context

46K

Memory

60.1 GB / 69.1 GB

Memory breakdown

Weights43.9 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsApertus v1.5 70B on MacBook Pro M4 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: 14.9 tok/s decode · 13.0s TTFT (warm) · 37 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	7.8 tok/s	13483 ms	46K
Coding	A	Tight fit	7.8 tok/s	24719 ms	46K
Agentic Coding	A	Tight fit	7.8 tok/s	35955 ms	46K
Reasoning	A	Tight fit	7.8 tok/s	29214 ms	46K
RAG	A	Tight fit	7.8 tok/s	44944 ms	46K

Inference speed

Apertus v1.5 70B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Apertus v1.5 70B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~17 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?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	16.7	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	14.9	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	13.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	11.5	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	11.0	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	10.9	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	8.8	Heavy offload
2× RTX 3090 24GB	48 GB	Q4_K_M	8.1	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	7.1	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	5.3	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	5.1	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.4	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	4.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.6	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too 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 Apertus v1.5 70B (72B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	10.4 GB	Very Low	A71
Q2_0_G128	1.71	19.2 GB	Low	A72
Q2_K	2	28.1 GB	Low	A74
Q3_K_S	3	35.3 GB	Low	A77
NVFP4	4	40.3 GB	Medium	A77
Q4_K_M	4	43.9 GB	Medium	A77
Q5_K_MBest for your GPU	5	51.8 GB	High	A77
Q6_K	6	59.0 GB	High	F0
Q8_0	8	77.0 GB	Very High	F0
F16	16	147.6 GB	Maximum	F0

Get started

Copy-paste commands to run Apertus v1.5 70B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "swiss-ai/Apertus-v1.5-70B" \
  --hf-file "Apertus-v1.5-70B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your MacBook Pro M4 Max 96GB can run

Model	Params	Grade	Decode	Capabilities
Command A 111B	111B	B	7.4 tok/s
Qwen3-Coder-Next	80B	A	23.2 tok/s

Frequently asked questions

Can MacBook Pro M4 Max 96GB run Apertus v1.5 70B?

Yes, MacBook Pro M4 Max 96GB can run Apertus v1.5 70B with a A grade (Tight fit). Expected decode speed: 7.8 tok/s.

How much VRAM does Apertus v1.5 70B need?

Apertus v1.5 70B (72B parameters) requires approximately 60.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Apertus v1.5 70B?

The recommended quantization for Apertus v1.5 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Apertus v1.5 70B run at on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Apertus v1.5 70B achieves approximately 7.8 tokens per second decode speed with a time-to-first-token of 24719ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run Apertus v1.5 70B for coding?

For coding workloads, Apertus v1.5 70B on MacBook Pro M4 Max 96GB receives a A grade with 7.8 tok/s and 46K context.

What context window can Apertus v1.5 70B use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Apertus v1.5 70B can safely use up to 46K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Apertus v1.5 70B feels slow on MacBook Pro M4 Max 96GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for Apertus v1.5 70B?

Not always. MacBook Pro M4 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 M4 Max 96GB See all hardware for Apertus v1.5 70B

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