Can Qwen 3.6 27B run on NVIDIA V100 32GB?

YES — Runs Great

S95Excellent

Estimated from fit model

Qwen 3.6 27B needs ~21.5 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~27 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 21.5 GB, 27.4 tok/s, Runs well

21.5 GB required32.0 GB available

67% VRAM used

Fit status

Runs well

Decode

27.4 tok/s

TTFT

7077 ms

Safe context

187K

Memory

21.5 GB / 32.0 GB

Memory breakdown

Weights16.5 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 3.6 27B 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: 27.4 tok/s decode · 7.1s TTFT (warm) · 68 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	27.4 tok/s	3860 ms	187K
Coding	S	Runs well	27.4 tok/s	7077 ms	187K
Agentic Coding	S	Runs well	27.4 tok/s	10294 ms	187K
Reasoning	S	Runs well	27.4 tok/s	8364 ms	187K
RAG	S	Runs well	27.4 tok/s	12867 ms	187K

Quantization options

How Qwen 3.6 27B (27B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	S89
Q3_K_S	3	13.2 GB	Low	S90
NVFP4	4	15.1 GB	Medium	S91
Q4_K_M	4	16.5 GB	Medium	S92
Q5_K_M	5	19.4 GB	High	S92
Q6_KBest for your GPU	6	22.1 GB	High	S91
Q8_0	8	28.9 GB	Very High	F0
F16	16	55.4 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.6 27B on your machine.

Run

lms load Qwen3.6-27B && lms server start

Your hardware

More models your NVIDIA V100 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	91.2 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run Qwen 3.6 27B?

Yes, NVIDIA V100 32GB can run Qwen 3.6 27B with a S grade (Runs well). Expected decode speed: 27.4 tok/s.

How much VRAM does Qwen 3.6 27B need?

Qwen 3.6 27B (27B parameters) requires approximately 21.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.6 27B?

The recommended quantization for Qwen 3.6 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.6 27B run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Qwen 3.6 27B achieves approximately 27.4 tokens per second decode speed with a time-to-first-token of 7077ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Qwen 3.6 27B for coding?

For coding workloads, Qwen 3.6 27B on NVIDIA V100 32GB receives a S grade with 27.4 tok/s and 187K context.

What context window can Qwen 3.6 27B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Qwen 3.6 27B can safely use up to 187K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Qwen 3.6 27B

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