Can Llama 3.1 70B run on MacBook Pro M4 Max 96GB?

YES — Tight Fit

A79Great

Estimated from fit model

Llama 3.1 70B needs ~58.9 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 58.9 GB, 15.3 tok/s, Tight fit

58.9 GB required69.1 GB available

85% VRAM used

Fit status

Tight fit

Decode

15.3 tok/s

TTFT

12657 ms

Safe context

50K

Memory

58.9 GB / 69.1 GB

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsLlama 3.1 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: 15.3 tok/s decode · 12.7s TTFT (warm) · 38 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	A	Runs well	15.3 tok/s	6904 ms	50K
Coding	A	Tight fit	15.3 tok/s	12657 ms	50K
Agentic Coding	A	Tight fit	15.3 tok/s	18410 ms	50K
Reasoning	A	Tight fit	15.3 tok/s	14958 ms	50K
RAG	A	Tight fit	15.3 tok/s	23013 ms	50K

Inference speed

Llama 3.1 70B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Llama 3.1 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 ~18 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	18.0	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	15.3	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	14.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	11.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	11.6	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	11.2	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	9.5	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	8.7	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	8.7	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	7.7	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	5.4	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.6	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	4.3	Too big
RTX 4090 24GB	24 GB	Q4_K_M	3.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.7	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.5	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.3	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 Llama 3.1 70B (70B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A76
Q3_K_S	3	34.3 GB	Low	A78
NVFP4	4	39.2 GB	Medium	A79
Q4_K_M	4	42.7 GB	Medium	A79
Q5_K_MBest for your GPU	5	50.4 GB	High	A79
Q6_K	6	57.4 GB	High	F0
Q8_0	8	74.9 GB	Very High	F0
F16	16	143.5 GB	Maximum	F0

Get started

Copy-paste commands to run Llama 3.1 70B on your machine.

Run

ollama run llama3.1

Your hardware

More models your MacBook Pro M4 Max 96GB can run

Model	Params	Grade	Decode
Command A 111B	111B	A	7.4 tok/s
Qwen 2.5 VL 72B	72B	S	14.9 tok/s
Qwen3-Coder-Next	80B	S	23.2 tok/s

Frequently asked questions

Can MacBook Pro M4 Max 96GB run Llama 3.1 70B?

Yes, MacBook Pro M4 Max 96GB can run Llama 3.1 70B with a A grade (Tight fit). Expected decode speed: 15.3 tok/s.

How much VRAM does Llama 3.1 70B need?

Llama 3.1 70B (70B parameters) requires approximately 58.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.1 70B?

The recommended quantization for Llama 3.1 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.1 70B run at on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Llama 3.1 70B achieves approximately 15.3 tokens per second decode speed with a time-to-first-token of 12657ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run Llama 3.1 70B for coding?

For coding workloads, Llama 3.1 70B on MacBook Pro M4 Max 96GB receives a A grade with 15.3 tok/s and 50K context.

What context window can Llama 3.1 70B use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Llama 3.1 70B can safely use up to 50K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for Llama 3.1 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 Llama 3.1 70B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/llama-3.1-70b-on-m4-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: