Can MPT-30B-Instruct run on Mac Studio M3 Ultra 96GB?

YES — Runs Great

A73Great

○Estimated from fit model

MPT-30B-Instruct needs ~56.6 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q5_K_M quantization, expect ~26 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) — 56.6 GB, 26.3 tok/s, Runs well

56.6 GB required69.1 GB available

82% VRAM used

Fit status

Runs well

Decode

26.3 tok/s

TTFT

7361 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 Mac Studio M3 Ultra 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: 26.3 tok/s decode · 7.4s TTFT (warm) · 66 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	26.3 tok/s	4015 ms	8K
Coding	A	Runs well	26.3 tok/s	7361 ms	8K
Agentic Coding	B	Very compromised (needs ~2.9 GB host RAM)	21.0 tok/s	13439 ms	8K
Reasoning	A	Runs well	26.3 tok/s	8700 ms	8K
RAG	B	Very compromised (needs ~2.9 GB host RAM)	21.0 tok/s	16799 ms	8K

Quantization options

How MPT-30B-Instruct (30B params) fits at each quantization level on Mac Studio M3 Ultra 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 Mac Studio M3 Ultra 96GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	84.2 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	70.8 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	77 tok/s
👁 Alibaba Qwen 3 32B	32B	S	31 tok/s
👁 Alibaba Qwen 3 30B A3B	30.5B	S	84.2 tok/s

Frequently asked questions

See all results for Mac Studio M3 Ultra 96GB See all hardware for MPT-30B-Instruct

URL: https://willitrunai.com/can-run/mpt-30b-instruct-on-m3-ultra-96gb

⇱ MPT-30B-Instruct on Mac Studio M3 Ultra 96GB? YES