Will It Run AI

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
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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

WorkloadGradeFitDecodeTTFTContext
ChatSTight fit136.8 tok/s772 ms99K
CodingSTight fit136.8 tok/s1416 ms99K
Agentic CodingSTight fit136.8 tok/s2059 ms99K
ReasoningSTight fit136.8 tok/s1673 ms99K
RAGSTight fit136.8 tok/s2574 ms99K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
91.7 GB
LowS86
Q3_K_S
3
115.2 GB
LowS86
NVFP4
4
131.6 GB
MediumS86
Q4_K_MBest for your GPU
4
143.4 GB
MediumS86
Q5_K_M
5
169.2 GB
HighF0
Q6_K
6
192.7 GB
HighF0
Q8_0
8
251.5 GB
Very HighF0
F16
16
481.7 GB
MaximumF0

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

ModelParamsGradeDecodeCapabilities
DeepSeekDeepSeek V4 Flash284BS144.8 tok/s

Frequently asked questions

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

Yes, NVIDIA B200 180GB can run Qwen 3 235B A22B with a S grade (Tight fit). Expected decode speed: 136.8 tok/s.

How much VRAM does Qwen 3 235B A22B need?

Qwen 3 235B A22B (235B parameters) requires approximately 165.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3 235B A22B?

The recommended quantization for Qwen 3 235B A22B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3 235B A22B run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Qwen 3 235B A22B achieves approximately 136.8 tokens per second decode speed with a time-to-first-token of 1416ms using Q4_K_M quantization.

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

For coding workloads, Qwen 3 235B A22B on NVIDIA B200 180GB receives a S grade with 136.8 tok/s and 99K context.

What context window can Qwen 3 235B A22B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Qwen 3 235B A22B can safely use up to 99K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GBSee all hardware for Qwen 3 235B A22B
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<iframe src="https://willitrunai.com/embed/qwen-3-235b-a22b-on-b200-180gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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