Can Qwen 3.5 35B A3B run on NVIDIA A16 64GB?

YES — Runs Great

S92Excellent

○Estimated from fit model

Qwen 3.5 35B A3B needs ~30.4 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~65 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 30.4 GB, 64.7 tok/s, Runs well

30.4 GB required64.0 GB available

48% VRAM used

Fit status

Runs well

Decode

64.7 tok/s

TTFT

2993 ms

Safe context

131K

Memory

30.4 GB / 64.0 GB

Memory breakdown

Weights21.3 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsQwen 3.5 35B A3B on NVIDIA A16 64GB
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: 64.7 tok/s decode · 3.0s TTFT (warm) · 162 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	64.7 tok/s	1633 ms	131K
Coding	S	Runs well	64.7 tok/s	2993 ms	131K
Agentic Coding	S	Runs well	64.7 tok/s	4354 ms	131K
Reasoning	S	Runs well	64.7 tok/s	3538 ms	131K
RAG	S	Runs well	64.7 tok/s	5442 ms	131K

Quantization options

How Qwen 3.5 35B A3B (35B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A84
Q3_K_S	3	17.2 GB	Low	A84
NVFP4	4	19.6 GB	Medium	S85
Q4_K_M	4	21.3 GB	Medium	S85
Q5_K_M	5	25.2 GB	High	S86
Q6_K	6	28.7 GB	High	S87
Q8_0Best for your GPU	8	37.5 GB	Very High	S89
F16	16	71.8 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.5 35B A3B on your machine.

Run

ollama run qwen3.5:35b-a3b

Frequently asked questions

See all results for NVIDIA A16 64GB See all hardware for Qwen 3.5 35B A3B

URL: https://willitrunai.com/can-run/qwen-3.5-35b-a3b-on-a16-64gb

⇱ Qwen 3.5 35B A3B on NVIDIA A16 64GB? YES