Can Qwen 3 235B A22B run on NVIDIA GB200 192GB?

YES — Tight Fit

S92Excellent

Estimated from fit model

Qwen 3 235B A22B needs ~166.3 GB VRAM. NVIDIA GB200 192GB has 192.0 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) — 166.3 GB, 136.8 tok/s, Tight fit

166.3 GB required192.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

136.8 tok/s

TTFT

1416 ms

Safe context

131K

Memory

166.3 GB / 192.0 GB

Memory breakdown

Weights143.4 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsQwen 3 235B A22B 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: 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	131K
Coding	S	Tight fit	136.8 tok/s	1416 ms	131K
Agentic Coding	S	Tight fit	136.8 tok/s	2059 ms	131K
Reasoning	S	Tight fit	136.8 tok/s	1673 ms	131K
RAG	S	Tight fit	136.8 tok/s	2574 ms	131K

Quantization options

How Qwen 3 235B A22B (235B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	91.7 GB	Low	S85
Q3_K_S	3	115.2 GB	Low	S86
NVFP4	4

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 GB200 192GB can run

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

Frequently asked questions

Can NVIDIA GB200 192GB run Qwen 3 235B A22B?

Yes, NVIDIA GB200 192GB 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 166.3 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 GB200 192GB?

On NVIDIA GB200 192GB, 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 GB200 192GB run Qwen 3 235B A22B for coding?

For coding workloads, Qwen 3 235B A22B on NVIDIA GB200 192GB receives a S grade with 136.8 tok/s and 131K context.

What context window can Qwen 3 235B A22B use on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, Qwen 3 235B A22B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA GB200 192GB See all hardware for Qwen 3 235B A22B

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<iframe src="https://willitrunai.com/embed/qwen-3-235b-a22b-on-gb200-192gb" 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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