VOOZH about

URL: https://willitrunai.com/can-run/qwen-3-235b-a22b-on-h100-pcie-80gb

⇱ Qwen 3 235B A22B on NVIDIA H100 PCIe 80GB? No — Alternatives

Can Qwen 3 235B A22B run on NVIDIA H100 PCIe 80GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

Qwen 3 235B A22B needs ~155.1 GB but NVIDIA H100 PCIe 80GB only has 80.0 GB. Try a smaller quantization or lighter model.

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

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

6.8 tok/s

TTFT

28585 ms

Safe context

4K

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 H100 PCIe 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: 6.8 tok/s decode · 28.6s TTFT (warm) · 17 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

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy6.9 tok/s15299 ms4K
CodingFToo heavy6.8 tok/s28585 ms4K
Agentic CodingFToo heavy6.5 tok/s43162 ms4K
ReasoningFToo heavy6.8 tok/s33782 ms4K
RAGFToo heavy6.5 tok/s53953 ms4K

Quantization options

How Qwen 3 235B A22B (235B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
91.7 GB
LowF0
Q3_K_S
3
115.2 GB
LowF0
NVFP4
4
131.6 GB
MediumF0
Q4_K_M
4
143.4 GB
MediumF0
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

Upgrade options

Hardware that runs Qwen 3 235B A22B well

👁 NVIDIA
NVIDIA B200 180GBBudget pick
180 GB VRAM (+100)8000 GB/s (+6000)
S
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 (+2800)
A
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 725%.

~$30,000 MSRP

👁 NVIDIA
NVIDIA H200 PCIe 141GBNVIDIA upgrade
141 GB VRAM (+61)4800 GB/s (+2800)
A
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 601%.

~$30,000 MSRP

Frequently asked questions

See all results for NVIDIA H100 PCIe 80GBSee all hardware for Qwen 3 235B A22B