Can MPT-30B-Instruct run on NVIDIA GB200 192GB?

YES — Runs Great

B70Good

○Estimated from fit model

MPT-30B-Instruct needs ~65.4 GB VRAM. NVIDIA GB200 192GB has 192.0 GB. With Q5_K_M quantization, expect ~317 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) — 65.4 GB, 317.3 tok/s, Runs well

65.4 GB required192.0 GB available

34% VRAM used

Fit status

Runs well

Decode

317.3 tok/s

TTFT

610 ms

Safe context

Memory

65.4 GB / 192.0 GB

Memory breakdown

Weights21.6 GB

KV Cache23.4 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsMPT-30B-Instruct on NVIDIA GB200 192GB
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: 317.3 tok/s decode · 610ms TTFT (warm) · 793 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	317.3 tok/s	350 ms	8K
Coding	B	Runs well	317.3 tok/s	610 ms	8K
Agentic Coding	A	Runs well	317.3 tok/s	887 ms	8K
Reasoning	B	Runs well	317.3 tok/s	721 ms	8K
RAG	A	Runs well	317.3 tok/s	1109 ms	8K

Quantization options

How MPT-30B-Instruct (30B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	B58
Q3_K_S	3	14.7 GB	Low	B58
NVFP4	4

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

Frequently asked questions

See all results for NVIDIA GB200 192GB See all hardware for MPT-30B-Instruct

URL: https://willitrunai.com/can-run/mpt-30b-instruct-on-gb200-192gb

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