Can Qwen 3 235B A22B run on NVIDIA B200 180GB?

YES — Tight Fit

S92Excellent

○Estimated from fit model

Qwen 3 235B A22B needs ~165.1 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~137 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 165.1 GB, 136.8 tok/s, Tight fit

165.1 GB required180.0 GB available

92% VRAM used

Fit status

Tight fit

Decode

136.8 tok/s

TTFT

1416 ms

Safe context

99K

Memory

165.1 GB / 180.0 GB

Memory breakdown

Weights143.4 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsQwen 3 235B A22B 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: 136.8 tok/s decode · 1.4s TTFT (warm) · 342 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	S	Tight fit	136.8 tok/s	772 ms	99K
Coding	S	Tight fit	136.8 tok/s	1416 ms	99K
Agentic Coding	S	Tight fit	136.8 tok/s	2059 ms	99K
Reasoning	S	Tight fit	136.8 tok/s	1673 ms	99K
RAG	S	Tight fit	136.8 tok/s	2574 ms	99K

Quantization options

How Qwen 3 235B A22B (235B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	91.7 GB	Low	S86
Q3_K_S	3	115.2 GB	Low	S86
NVFP4	4	131.6 GB	Medium	S86
Q4_K_MBest for your GPU	4	143.4 GB	Medium	S86
Q5_K_M	5	169.2 GB	High	F0
Q6_K	6	192.7 GB	High	F0
Q8_0	8	251.5 GB	Very High	F0
F16	16	481.7 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3 235B A22B on your machine.

Run

lms load Qwen3-235B-A22B-Instruct-2507 && lms server start

Your hardware

More models your NVIDIA B200 180GB can run

Model	Params	Grade	Decode	Capabilities
👁 DeepSeek DeepSeek V4 Flash	284B	S	144.8 tok/s

Frequently asked questions

See all results for NVIDIA B200 180GB See all hardware for Qwen 3 235B A22B

URL: https://willitrunai.com/can-run/qwen-3-235b-a22b-on-b200-180gb

⇱ Qwen 3 235B A22B on NVIDIA B200 180GB? TIGHT FIT