Can Qwen 2.5 Math 72B run on NVIDIA B200 180GB?

YES — Runs Great

B63Good

○Estimated from fit model

Qwen 2.5 Math 72B needs ~67.7 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~153 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 67.7 GB, 166.4 tok/s, Runs well

67.7 GB required180.0 GB available

38% VRAM used

Fit status

Runs well

Decode

166.4 tok/s

TTFT

1164 ms

Safe context

Memory

67.7 GB / 180.0 GB

Memory breakdown

Weights43.9 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsQwen 2.5 Math 72B on NVIDIA B200 180GB
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: 166.4 tok/s decode · 1.2s TTFT (warm) · 416 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	166.4 tok/s	635 ms	4K
Coding	B	Runs well	153.0 tok/s	1265 ms	4K
Agentic Coding	B	Runs well	166.4 tok/s	1692 ms	4K
Reasoning	B	Runs well	166.4 tok/s	1375 ms	4K
RAG	B	Runs well	166.4 tok/s	2115 ms	4K

Quantization options

How Qwen 2.5 Math 72B (72B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	C52
Q3_K_S	3	35.3 GB	Low	C53
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 Math 72B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "Qwen/Qwen2.5-Math-72B-Instruct" \
 --hf-file "Qwen2.5-Math-72B-Instruct-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Frequently asked questions

See all results for NVIDIA B200 180GB See all hardware for Qwen 2.5 Math 72B

URL: https://willitrunai.com/can-run/qwen-2.5-math-72b-on-b200-180gb

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