Can Qwen 3.5 122B A10B run on RTX PRO 6000 Blackwell Workstation Edition 96GB?

YES — Tight Fit

S95Excellent

○Estimated from fit model

Qwen 3.5 122B A10B needs ~87.4 GB VRAM. RTX PRO 6000 Blackwell Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~61 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 87.4 GB, 60.5 tok/s, Tight fit

87.4 GB required96.0 GB available

91% VRAM used

Fit status

Tight fit

Decode

60.5 tok/s

TTFT

3199 ms

Safe context

73K

Memory

87.4 GB / 96.0 GB

Memory breakdown

Weights74.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsQwen 3.5 122B A10B on RTX PRO 6000 Blackwell Workstation Edition 96GB
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: 60.5 tok/s decode · 3.2s TTFT (warm) · 151 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	Tight fit	60.5 tok/s	1745 ms	73K
Coding	S	Tight fit	60.5 tok/s	3199 ms	73K
Agentic Coding	S	Tight fit	60.5 tok/s	4653 ms	73K
Reasoning	S	Tight fit	60.5 tok/s	3781 ms	73K
RAG	S	Tight fit	60.5 tok/s	5817 ms	73K

Quantization options

How Qwen 3.5 122B A10B (122B params) fits at each quantization level on RTX PRO 6000 Blackwell Workstation Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	47.6 GB	Low	S90
Q3_K_S	3	59.8 GB	Low	S90
NVFP4	4	68.3 GB	Medium	S90
Q4_K_MBest for your GPU	4	74.4 GB	Medium	S90
Q5_K_M	5	87.8 GB	High	F0
Q6_K	6	100.0 GB	High	F0
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 RTX PRO 6000 Blackwell Workstation Edition 96GB can run

Model	Params	Grade	Decode	Capabilities
👁 Mistral Devstral 2 123B Instruct	123B	S	21.8 tok/s

Frequently asked questions

See all results for RTX PRO 6000 Blackwell Workstation Edition 96GB See all hardware for Qwen 3.5 122B A10B

URL: https://willitrunai.com/can-run/qwen-3.5-122b-a10b-on-rtx-pro-6000-blackwell-96gb

⇱ Qwen 3.5 122B A10B on RTX PRO 6000 Blackwell Workstation Ed…