Can Qwen 3.5 35B A3B run on NVIDIA V100 32GB?

YES — Tight Fit

S95Excellent

Estimated from fit model

Qwen 3.5 35B A3B needs ~27.2 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~83 tok/s.

Runtime: OllamaCapacity: TightBandwidth: HighStack: 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) — 27.2 GB, 83.3 tok/s, Tight fit

27.2 GB required32.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

83.3 tok/s

TTFT

2323 ms

Safe context

68K

Memory

27.2 GB / 32.0 GB

Memory breakdown

Weights21.3 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 3.5 35B A3B on NVIDIA V100 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: 83.3 tok/s decode · 2.3s TTFT (warm) · 208 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	83.3 tok/s	1267 ms	68K
Coding	S	Tight fit	83.3 tok/s	2323 ms	68K
Agentic Coding	S	Tight fit	83.3 tok/s	3379 ms	68K
Reasoning	S	Tight fit	83.3 tok/s	2746 ms	68K
RAG	S	Tight fit	76.6 tok/s	4594 ms	68K

Quantization options

How Qwen 3.5 35B A3B (35B params) fits at each quantization level on NVIDIA V100 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	S91
NVFP4	4	19.6 GB	Medium	S91
Q4_K_M	4	21.3 GB	Medium	S90
Q5_K_MBest for your GPU	5	25.2 GB	High	S90
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 3.5 35B A3B on your machine.

Run

ollama run qwen3.5:35b-a3b

Frequently asked questions

Can NVIDIA V100 32GB run Qwen 3.5 35B A3B?

Yes, NVIDIA V100 32GB can run Qwen 3.5 35B A3B with a S grade (Tight fit). Expected decode speed: 83.3 tok/s.

How much VRAM does Qwen 3.5 35B A3B need?

Qwen 3.5 35B A3B (35B parameters) requires approximately 27.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 35B A3B?

The recommended quantization for Qwen 3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 35B A3B run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Qwen 3.5 35B A3B achieves approximately 83.3 tokens per second decode speed with a time-to-first-token of 2323ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Qwen 3.5 35B A3B for coding?

For coding workloads, Qwen 3.5 35B A3B on NVIDIA V100 32GB receives a S grade with 83.3 tok/s and 68K context.

What context window can Qwen 3.5 35B A3B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Qwen 3.5 35B A3B can safely use up to 68K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Qwen 3.5 35B A3B

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