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

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen 3 235B A22B needs ~155.1 GB but NVIDIA H100 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

16.4 tok/s

TTFT

11818 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 H100 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: 16.4 tok/s decode · 11.8s TTFT (warm) · 41 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	16.6 tok/s	6348 ms	4K
Coding	F	Too heavy	16.4 tok/s	11818 ms	4K
Agentic Coding	F	Too heavy	15.9 tok/s	17718 ms	4K
Reasoning	F	Too heavy	16.4 tok/s	13967 ms	4K
RAG	F	Too heavy	15.9 tok/s	22147 ms	4K

Inference speed

Qwen 3 235B A22B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3 235B A22B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~11 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	11.3	Tight
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	4.7	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	4.4	Too big
RTX 5090 32GB	32 GB	Q4_K_M	3.7	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	3.6	Too big
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	3.5	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	3.2	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.5	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.3	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.2	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.1	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.1	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.0	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen 3 235B A22B (235B params) fits at each quantization level on NVIDIA H100 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	131.6 GB	Medium	F0
Q4_K_M	4	143.4 GB	Medium	F0
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

Opciones de mejora

Hardware que ejecuta bien Qwen 3 235B A22B

NVIDIA B200 180GBOpción económica

180 GB VRAM (+100)8000 GB/s (+4650)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.136.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$30,000 MSRP

NVIDIA H200 141GBMejor relación calidad-precio

141 GB VRAM (+61)4800 GB/s (+1450)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.56.1 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 242%.

~$30,000 MSRP

NVIDIA H200 PCIe 141GBMejora NVIDIA

141 GB VRAM (+61)4800 GB/s (+1450)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.47.7 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 191%.

~$30,000 MSRP

Frequently asked questions

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

No, Qwen 3 235B A22B requires more memory than NVIDIA H100 80GB provides.

How much VRAM does Qwen 3 235B A22B need?

Qwen 3 235B A22B (235B parameters) requires approximately 155.1 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 H100 80GB?

On NVIDIA H100 80GB, Qwen 3 235B A22B achieves approximately 16.4 tokens per second decode speed with a time-to-first-token of 11818ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Qwen 3 235B A22B for coding?

For coding workloads, Qwen 3 235B A22B on NVIDIA H100 80GB receives a F grade with 16.4 tok/s and 4K context.

What context window can Qwen 3 235B A22B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Qwen 3 235B A22B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3 235B A22B feels slow on NVIDIA H100 80GB?

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.

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

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