Can Qwen3-VL 30B A3B Instruct run on NVIDIA H100 80GB?

YES — Runs Great

S92Excellent

Estimated from fit model

Qwen3-VL 30B A3B Instruct needs ~30.2 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~250 tok/s.

Runtime: vLLMCapacity: RoomyBandwidth: HighStack: OptimizedBottleneck: 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) — 30.2 GB, 249.6 tok/s, Runs well

30.2 GB required80.0 GB available

38% VRAM used

Fit status

Runs well

Decode

249.6 tok/s

TTFT

776 ms

Safe context

256K

Memory

30.2 GB / 80.0 GB

Memory breakdown

Weights18.3 GB

KV Cache1.5 GB

Runtime2.4 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen3-VL 30B A3B Instruct 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: 249.6 tok/s decode · 776ms TTFT (warm) · 624 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	Runs well	249.6 tok/s	423 ms	256K
Coding	S	Runs well	249.6 tok/s	776 ms	256K
Agentic Coding	S	Runs well	249.6 tok/s	1128 ms	256K
Reasoning	S	Runs well	249.6 tok/s	917 ms	256K
RAG	S	Runs well	249.6 tok/s	1410 ms	256K

Inference speed

Qwen3-VL 30B A3B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen3-VL 30B A3B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~143 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?
RTX 5090 32GB	32 GB	Q4_K_M	142.7	Fits
RTX 4090 24GB	24 GB	Q4_K_M	119.8	Offloads

Quantization options

How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A83
Q3_K_S	3	14.7 GB	Low	A83
NVFP4	4

Get started

Copy-paste commands to run Qwen3-VL 30B A3B Instruct on your machine.

Run

lms load Qwen3-VL-30B-A3B-Instruct && lms server start

Your hardware

More models your NVIDIA H100 80GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	241.3 tok/s
Qwen 3.6 35B A3B	35B	S	202.8 tok/s

Frequently asked questions

Can NVIDIA H100 80GB run Qwen3-VL 30B A3B Instruct?

Yes, NVIDIA H100 80GB can run Qwen3-VL 30B A3B Instruct with a S grade (Runs well). Expected decode speed: 249.6 tok/s.

How much VRAM does Qwen3-VL 30B A3B Instruct need?

Qwen3-VL 30B A3B Instruct (30B parameters) requires approximately 30.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-VL 30B A3B Instruct?

The recommended quantization for Qwen3-VL 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-VL 30B A3B Instruct run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Qwen3-VL 30B A3B Instruct achieves approximately 249.6 tokens per second decode speed with a time-to-first-token of 776ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Qwen3-VL 30B A3B Instruct for coding?

For coding workloads, Qwen3-VL 30B A3B Instruct on NVIDIA H100 80GB receives a S grade with 249.6 tok/s and 256K context.

What context window can Qwen3-VL 30B A3B Instruct use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Qwen3-VL 30B A3B Instruct can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GB See all hardware for Qwen3-VL 30B A3B Instruct

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