Can Qwen 2.5 Math 72B run on RTX PRO 6000 Blackwell Server Edition 96GB?

YES — Runs Great

B65Good

○Estimated from fit model

Qwen 2.5 Math 72B needs ~59.3 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~33 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) — 59.3 GB, 33.2 tok/s, Runs well

59.3 GB required96.0 GB available

62% VRAM used

Fit status

Runs well

Decode

33.2 tok/s

TTFT

5828 ms

Safe context

Memory

59.3 GB / 96.0 GB

Memory breakdown

Weights43.9 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Math 72B on RTX PRO 6000 Blackwell Server Edition 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: 33.2 tok/s decode · 5.8s TTFT (warm) · 83 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	33.2 tok/s	3179 ms	4K
Coding	B	Runs well	33.2 tok/s	5828 ms	4K
Agentic Coding	B	Runs well	33.2 tok/s	8478 ms	4K
Reasoning	B	Runs well	33.2 tok/s	6888 ms	4K
RAG	B	Runs well	33.2 tok/s	10597 ms	4K

Quantization options

How Qwen 2.5 Math 72B (72B params) fits at each quantization level on RTX PRO 6000 Blackwell Server Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	B56
Q3_K_S	3	35.3 GB	Low	B57
NVFP4	4	40.3 GB	Medium	B58
Q4_K_M	4	43.9 GB	Medium	B59
Q5_K_M	5	51.8 GB	High	B61
Q6_K	6	59.0 GB	High	B61
Q8_0Best for your GPU	8	77.0 GB	Very High	B61
F16	16	147.6 GB	Maximum	F0

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

Upgrade options