Can Qwen 3.5 122B A10B run on NVIDIA H200 141GB?

YES — Runs Great

S98Excellent

○Estimated from fit model

Qwen 3.5 122B A10B needs ~91.9 GB VRAM. NVIDIA H200 141GB has 141.0 GB. With Q4_K_M quantization, expect ~162 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 91.9 GB, 162.1 tok/s, Runs well

91.9 GB required141.0 GB available

65% VRAM used

Fit status

Runs well

Decode

162.1 tok/s

TTFT

1194 ms

Safe context

131K

Memory

91.9 GB / 141.0 GB

Memory breakdown

Weights74.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsQwen 3.5 122B A10B on NVIDIA H200 141GB
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: 162.1 tok/s decode · 1.2s TTFT (warm) · 405 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	162.1 tok/s	651 ms	131K
Coding	S	Runs well	162.1 tok/s	1194 ms	131K
Agentic Coding	S	Runs well	162.1 tok/s	1737 ms	131K
Reasoning	S	Runs well	162.1 tok/s	1412 ms	131K
RAG	S	Runs well	162.1 tok/s	2172 ms	131K

Quantization options

How Qwen 3.5 122B A10B (122B params) fits at each quantization level on NVIDIA H200 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	47.6 GB	Low	S86
Q3_K_S	3	59.8 GB	Low	S88
NVFP4	4	68.3 GB	Medium	S89
Q4_K_M	4	74.4 GB	Medium	S90
Q5_K_M	5	87.8 GB	High	S90
Q6_KBest for your GPU	6	100.0 GB	High	S90
Q8_0	8	130.5 GB	Very High	F0
F16	16	250.1 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.5 122B A10B on your machine.

Run

lms load Qwen3.5-122B-A10B-Instruct && lms server start

Your hardware

More models your NVIDIA H200 141GB can run

Model	Params	Grade	Decode	Capabilities
👁 Mistral Devstral 2 123B Instruct	123B	S	58.4 tok/s

Frequently asked questions

See all results for NVIDIA H200 141GB See all hardware for Qwen 3.5 122B A10B

URL: https://willitrunai.com/can-run/qwen-3.5-122b-a10b-on-h200-141gb

⇱ Qwen 3.5 122B A10B on NVIDIA H200 141GB? YES