Can Qwen 3 235B A22B run on NVIDIA A100 80GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Qwen 3 235B A22B needs ~155.1 GB but NVIDIA A100 80GB only has 80.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: StandardBottleneck: Memory capacity

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) — 155.1 GB, exceeds 80.0 GB available

155.1 GB required80.0 GB available

194% VRAM needed

75.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.0 tok/s

TTFT

19417 ms

Safe context

Memory

155.1 GB / 80.0 GB

Offload

50%

Memory breakdown

Weights143.4 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3 235B A22B on NVIDIA A100 80GB
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: 10.0 tok/s decode · 19.4s TTFT (warm) · 25 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 155.1 GB, but this setup only exposes 80.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	10.1 tok/s	10430 ms	4K
Coding	F	Too heavy	10.0 tok/s	19417 ms	4K
Agentic Coding	F	Too heavy	8.8 tok/s	31838 ms	4K
Reasoning	F	Too heavy	10.0 tok/s	22947 ms	4K
RAG	F	Too heavy	9.7 tok/s	36387 ms	4K

Quantization options

How Qwen 3 235B A22B (235B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	91.7 GB	Low	F0
Q3_K_S	3	115.2 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Qwen 3 235B A22B well

👁 NVIDIA
NVIDIA B200 180GBBudget pick

180 GB VRAM (+100)8000 GB/s (+5961)

Makes the model fit on the accelerator instead of staying completely out of reach.136.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$30,000 MSRP

👁 NVIDIA
NVIDIA H200 141GBBest value

141 GB VRAM (+61)4800 GB/s (+2761)

Makes the model fit on the accelerator instead of staying completely out of reach.56.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 461%.

~$30,000 MSRP

👁 NVIDIA
NVIDIA H200 PCIe 141GBNVIDIA upgrade

141 GB VRAM (+61)4800 GB/s (+2761)

Makes the model fit on the accelerator instead of staying completely out of reach.47.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 377%.

~$30,000 MSRP

Frequently asked questions

See all results for NVIDIA A100 80GB See all hardware for Qwen 3 235B A22B

URL: https://willitrunai.com/can-run/qwen-3-235b-a22b-on-a100-80gb

⇱

Can Qwen 3 235B A22B run on NVIDIA A100 80GB?

NO — Won't Fit

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Hardware that runs Qwen 3 235B A22B well

Frequently asked questions