Can MPT-30B-Instruct run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

A72Great

Estimated from fit model

MPT-30B-Instruct needs ~60.1 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q5_K_M quantization, expect ~22 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 60.1 GB, 21.9 tok/s, Runs well

60.1 GB required92.2 GB available

65% VRAM used

Fit status

Runs well

Decode

21.9 tok/s

TTFT

8836 ms

Safe context

Memory

60.1 GB / 92.2 GB

Memory breakdown

Weights21.6 GB

KV Cache23.4 GB

Runtime1.2 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsMPT-30B-Instruct on Mac Studio M2 Ultra 128GB
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: 21.9 tok/s decode · 8.8s TTFT (warm) · 55 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	B	Runs well	21.9 tok/s	4819 ms	8K
Coding	A	Runs well	21.9 tok/s	8836 ms	8K
Agentic Coding	B	Tight fit	21.9 tok/s	12852 ms	8K
Reasoning	A	Runs well	21.9 tok/s	10442 ms	8K
RAG	B	Tight fit	21.9 tok/s	16065 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 Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	B60
Q3_K_S	3	14.7 GB	Low	B61
NVFP4	4	16.8 GB	Medium	B61
Q4_K_M	4	18.3 GB	Medium	B61
Q5_K_M	5	21.6 GB	High	B62
Q6_K	6	24.6 GB	High	B62
Q8_0	8	32.1 GB	Very High	B64
F16Best for your GPU	16	61.5 GB	Maximum	B68

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 Mac Studio M2 Ultra 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	6.3 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.2 tok/s
Qwen 3.5 122B A10B	122B	S	28.9 tok/s
Qwen 3.6 35B A3B	35B	S	59 tok/s
Qwen 3.5 35B A3B	35B	S	64.1 tok/s

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run MPT-30B-Instruct?

Yes, Mac Studio M2 Ultra 128GB can run MPT-30B-Instruct with a A grade (Runs well). Expected decode speed: 21.9 tok/s.

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

MPT-30B-Instruct (30B parameters) requires approximately 60.1 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 Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, MPT-30B-Instruct achieves approximately 21.9 tokens per second decode speed with a time-to-first-token of 8836ms using Q5_K_M quantization.

Can Mac Studio M2 Ultra 128GB run MPT-30B-Instruct for coding?

For coding workloads, MPT-30B-Instruct on Mac Studio M2 Ultra 128GB receives a A grade with 21.9 tok/s and 8K context.

What context window can MPT-30B-Instruct use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, 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 Mac Studio M2 Ultra 128GB as fast as VRAM for MPT-30B-Instruct?

Not always. Mac Studio M2 Ultra 128GB 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 Mac Studio M2 Ultra 128GB See all hardware for MPT-30B-Instruct

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