Can Qwen 3.6 35B A3B run on RTX 5000 Ada 32GB?

YES — With Offload

S95Excellent

○Estimated from fit model

Qwen 3.6 35B A3B needs ~30.5 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: TransformersCapacity: OffloadBandwidth: MediumStack: 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) — 30.5 GB, 58.6 tok/s, Runs with offload

30.5 GB required32.0 GB available

95% VRAM used

Fit status

Runs with offload

Decode

58.6 tok/s

TTFT

3306 ms

Safe context

22K

Memory

30.5 GB / 32.0 GB

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime1.8 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 3.6 35B A3B on RTX 5000 Ada 32GB
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: 58.6 tok/s decode · 3.3s TTFT (warm) · 146 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.

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	S	Tight fit	58.6 tok/s	1803 ms	22K
Coding	S	Runs with offload	58.6 tok/s	3306 ms	22K
Agentic Coding	A	Runs with offload (needs ~1.6 GB host RAM)	37.4 tok/s	7536 ms	22K
Reasoning	S	Runs with offload	58.6 tok/s	3907 ms	22K
RAG	A	Runs with offload (needs ~1.6 GB host RAM)	37.4 tok/s	9420 ms	22K

Quantization options

How Qwen 3.6 35B A3B (35B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	S90
Q3_K_S	3	17.2 GB	Low	S92
NVFP4	4	19.6 GB	Medium	S91
Q4_K_M	4	21.3 GB	Medium	S91
Q5_K_MBest for your GPU	5	25.2 GB	High	S91
Q6_K	6	28.7 GB	High	F0
Q8_0	8	37.5 GB	Very High	F0
F16	16	71.8 GB	Maximum	F0

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

Frequently asked questions

See all results for RTX 5000 Ada 32GB See all hardware for Qwen 3.6 35B A3B

URL: https://willitrunai.com/can-run/qwen-3.6-35b-a3b-on-rtx-5000-ada-32gb

⇱ Qwen 3.6 35B A3B on RTX 5000 Ada 32GB? YES