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

YES — Runs Great

S93Excellent

○Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~29.3 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~391 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 29.3 GB, 425.5 tok/s, Runs well

29.3 GB required80.0 GB available

37% VRAM used

Fit status

Runs well

Decode

425.5 tok/s

TTFT

455 ms

Safe context

256K

Memory

29.3 GB / 80.0 GB

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct on NVIDIA H100 80GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
 """Sort a list using the quicksort algorithm.

 Args:
 arr: List of integers to sort.

 Returns:
 A new sorted list.
 """
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 425.5 tok/s decode · 455ms TTFT (warm) · 1064 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	391.3 tok/s	350 ms	256K
Coding	S	Runs well	391.3 tok/s	495 ms	256K
Agentic Coding	S	Runs well	391.3 tok/s	720 ms	256K
Reasoning	S	Runs well	391.3 tok/s	585 ms	256K
RAG	S	Runs well	391.3 tok/s	900 ms	256K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	A84
Q3_K_S	3	14.9 GB	Low	A84
NVFP4	4

Get started

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

Run

ollama run qwen3-coder

Your hardware

More models your NVIDIA H100 80GB can run

Model	Params	Grade	Decode	Capabilities
👁 Mistral Devstral 2 123B Instruct	123B	A	28.9 tok/s

Frequently asked questions

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

URL: https://willitrunai.com/can-run/qwen-3-coder-30b-a3b-on-h100-80gb

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