Can Qwen 3.6 35B A3B run on Quadro RTX 6000 24GB?

YES — With Q2_K

S95Excellent

○Estimated from fit model

Qwen 3.6 35B A3B needs ~22.6 GB VRAM. Quadro RTX 6000 24GB has 24.0 GB. With Q2_K quantization, expect ~60 tok/s.

Runtime: vLLMCapacity: TightBandwidth: MediumStack: 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.

Qwen 3.6 35B A3B at Q4_K_M needs 30.3 GB — too much for Quadro RTX 6000 24GB (24.0 GB). Runs at Q2_K (22.6 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 30.3 GB, exceeds 24.0 GB available

30.3 GB required24.0 GB available

126% VRAM needed

6.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

19.6 tok/s

TTFT

9854 ms

Safe context

Memory

30.3 GB / 24.0 GB

Offload

20%

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime2.4 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3.6 35B A3B on Quadro RTX 6000 24GB
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: 19.6 tok/s decode · 9.9s TTFT (warm) · 49 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	22.9 tok/s	4607 ms	4K
Coding	F	Too heavy	19.6 tok/s	9854 ms	4K
Agentic Coding	F	Too heavy	14.9 tok/s	18949 ms	4K
Reasoning	F	Too heavy	19.6 tok/s	11645 ms	4K
RAG	F	Too heavy	14.9 tok/s	23686 ms	4K

Quantization options

How Qwen 3.6 35B A3B (35B params) fits at each quantization level on Quadro RTX 6000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	S92
Q3_K_SBest for your GPU	3	17.2 GB	Low	S92

Get started

Copy-paste commands to run Qwen 3.6 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "Qwen/Qwen3.6-35B-A3B" \
 --hf-file "Qwen3.6-35B-A3B-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Upgrade options

Hardware that runs Qwen 3.6 35B A3B well

👁 NVIDIA
RTX A6000 48GBBudget pick

48 GB VRAM (+24)768 GB/s (+96)

Makes the model fit on the accelerator instead of staying completely out of reach.56.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$4,650 MSRP

👁 NVIDIA
RTX PRO 5000 Blackwell 48GBBest value

48 GB VRAM (+24)1344 GB/s (+672)

Makes the model fit on the accelerator instead of staying completely out of reach.109 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$4,999 MSRP

👁 NVIDIA
NVIDIA L40 48GBNVIDIA upgrade

48 GB VRAM (+24)864 GB/s (+192)

Makes the model fit on the accelerator instead of staying completely out of reach.65.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$5,500 MSRP

Frequently asked questions

See all results for Quadro RTX 6000 24GB See all hardware for Qwen 3.6 35B A3B

URL: https://willitrunai.com/can-run/qwen-3.6-35b-a3b-on-quadro-rtx-6000-24gb

⇱

Can Qwen 3.6 35B A3B run on Quadro RTX 6000 24GB?

YES — With Q2_K

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Qwen 3.6 35B A3B well

Frequently asked questions