Can Qwen3-Coder-Next run on NVIDIA H100 80GB?

YES — Runs Great

S97Excellent

○Estimated from fit model

Qwen3-Coder-Next needs ~59.5 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~190 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) — 59.5 GB, 190.0 tok/s, Runs well

59.5 GB required80.0 GB available

74% VRAM used

Fit status

Runs well

Decode

190.0 tok/s

TTFT

1019 ms

Safe context

240K

Memory

59.5 GB / 80.0 GB

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next 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: 190.0 tok/s decode · 1.0s TTFT (warm) · 475 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	190.0 tok/s	556 ms	240K
Coding	S	Runs well	190.0 tok/s	1019 ms	240K
Agentic Coding	S	Runs well	190.0 tok/s	1482 ms	240K
Reasoning	S	Runs well	190.0 tok/s	1204 ms	240K
RAG	S	Runs well	190.0 tok/s	1852 ms	240K

Quantization options

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

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	A85
Q3_K_S	3	39.2 GB	Low	S87
NVFP4	4	44.8 GB	Medium	S88
Q4_K_M	4	48.8 GB	Medium	S88
Q5_K_M	5	57.6 GB	High	S88
Q6_KBest for your GPU	6	65.6 GB	High	S88
Q8_0	8	85.6 GB	Very High	F0
F16	16	164.0 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen3-Coder-Next on your machine.

Run

ollama run qwen3-coder-next

Your hardware

More models your NVIDIA H100 80GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	A	28.9 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	85.5 tok/s
👁 Mistral Mistral Small 4 119B	119B	A	90.8 tok/s
👁 OpenAI GPT-OSS 120B	117B	A	32.8 tok/s
👁 Cohere Command A 111B	111B	S	38.1 tok/s

Frequently asked questions

See all results for NVIDIA H100 80GB See all hardware for Qwen3-Coder-Next

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

⇱ Qwen3-Coder-Next on NVIDIA H100 80GB? YES