Can MPT-30B-Instruct run on MacBook Pro M4 Max 96GB?

YES — Runs Great

A74Great

Estimated from fit model

MPT-30B-Instruct needs ~56.6 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q5_K_M quantization, expect ~28 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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

Q5_K_M (High quality) — 56.6 GB, 28.4 tok/s, Runs well

56.6 GB required69.1 GB available

82% VRAM used

Fit status

Runs well

Decode

28.4 tok/s

TTFT

6826 ms

Safe context

Memory

56.6 GB / 69.1 GB

Memory breakdown

Weights21.6 GB

KV Cache23.4 GB

Runtime1.2 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsMPT-30B-Instruct 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: 28.4 tok/s decode · 6.8s TTFT (warm) · 71 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	28.4 tok/s	3723 ms	8K
Coding	A	Runs well	28.4 tok/s	6826 ms	8K
Agentic Coding	B	Very compromised (needs ~2.9 GB host RAM)	22.6 tok/s	12462 ms	8K
Reasoning	A	Runs well	28.4 tok/s	8067 ms	8K
RAG	B	Very compromised (needs ~2.9 GB host RAM)	22.6 tok/s	15577 ms	8K

Inference speed

MPT-30B-Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for MPT-30B-Instruct at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~28 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?
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	28.4	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	26.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	22.7	Heavy offload
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	21.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	20.8	Fits
RTX 5090 32GB	32 GB	Q5_K_M	17.9	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	10.5	Too big
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	9.1	Heavy offload
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	8.3	Heavy offload
RTX 4090 24GB	24 GB	Q5_K_M	6.1	Too big
RX 7900 XTX 24GB	24 GB	Q5_K_M	5.5	Too big
RTX 3090 24GB	24 GB	Q5_K_M	5.2	Too big
RTX 4080 Super 16GB	16 GB	Q5_K_M	4.3	Too big
RTX 4070 12GB	12 GB	Q5_K_M	2.7	Too big
RTX 3060 12GB	12 GB	Q5_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q5_K_M	2.0	Too big

Estimates for single-stream decoding at Q5_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 MPT-30B-Instruct (30B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	B62
Q3_K_S	3	14.7 GB	Low	B62
NVFP4	4	16.8 GB	Medium	B63
Q4_K_M	4	18.3 GB	Medium	B63
Q5_K_M	5	21.6 GB	High	B64
Q6_K	6	24.6 GB	High	B65
Q8_0Best for your GPU	8	32.1 GB	Very High	B66
F16	16	61.5 GB	Maximum	F0

Get started

Copy-paste commands to run MPT-30B-Instruct on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mosaicml/mpt-30b-instruct" \
  --hf-file "mpt-30b-instruct-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your MacBook Pro M4 Max 96GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	52 tok/s
Qwen 3.6 35B A3B	35B	S	43.7 tok/s
Qwen 3.5 35B A3B	35B	S	47.5 tok/s
Qwen 3 32B	32B	S	33.5 tok/s
Qwen 3 30B A3B	30.5B	S	52 tok/s

Frequently asked questions

Can MacBook Pro M4 Max 96GB run MPT-30B-Instruct?

Yes, MacBook Pro M4 Max 96GB can run MPT-30B-Instruct with a A grade (Runs well). Expected decode speed: 28.4 tok/s.

How much VRAM does MPT-30B-Instruct need?

MPT-30B-Instruct (30B parameters) requires approximately 56.6 GB of memory with Q5_K_M quantization.

What is the best quantization for MPT-30B-Instruct?

The recommended quantization for MPT-30B-Instruct is Q5_K_M, which balances quality and memory efficiency.

What speed will MPT-30B-Instruct run at on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, MPT-30B-Instruct achieves approximately 28.4 tokens per second decode speed with a time-to-first-token of 6826ms using Q5_K_M quantization.

Can MacBook Pro M4 Max 96GB run MPT-30B-Instruct for coding?

For coding workloads, MPT-30B-Instruct on MacBook Pro M4 Max 96GB receives a A grade with 28.4 tok/s and 8K context.

What context window can MPT-30B-Instruct use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, MPT-30B-Instruct can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for MPT-30B-Instruct?

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 MPT-30B-Instruct

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