Can Qwen 3.6 27B run on NVIDIA A100 80GB?

YES — Runs Great

S91Excellent

Estimated from fit model

Qwen 3.6 27B needs ~26.3 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~70 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) — 26.3 GB, 70.0 tok/s, Runs well

26.3 GB required80.0 GB available

33% VRAM used

Fit status

Runs well

Decode

70.0 tok/s

TTFT

2765 ms

Safe context

262K

Memory

26.3 GB / 80.0 GB

Memory breakdown

Weights16.5 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen 3.6 27B on NVIDIA A100 80GB
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: 70.0 tok/s decode · 2.8s TTFT (warm) · 175 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	70.0 tok/s	1508 ms	262K
Coding	S	Runs well	70.0 tok/s	2765 ms	262K
Agentic Coding	S	Runs well	70.0 tok/s	4022 ms	262K
Reasoning	S	Runs well	70.0 tok/s	3268 ms	262K
RAG	S	Runs well	70.0 tok/s	5028 ms	262K

Quantization options

How Qwen 3.6 27B (27B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A83
Q3_K_S	3	13.2 GB	Low	A83
NVFP4	4	15.1 GB	Medium	A83
Q4_K_M	4	16.5 GB	Medium	A84
Q5_K_M	5	19.4 GB	High	A84
Q6_K	6	22.1 GB	High	A85
Q8_0	8	28.9 GB	Very High	S86
F16Best for your GPU	16	55.4 GB	Maximum	S90

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 A100 80GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	A	17.7 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	259 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Qwen 3.6 27B?

Yes, NVIDIA A100 80GB can run Qwen 3.6 27B with a S grade (Runs well). Expected decode speed: 70.0 tok/s.

How much VRAM does Qwen 3.6 27B need?

Qwen 3.6 27B (27B parameters) requires approximately 26.3 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 A100 80GB?

On NVIDIA A100 80GB, Qwen 3.6 27B achieves approximately 70.0 tokens per second decode speed with a time-to-first-token of 2765ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Qwen 3.6 27B for coding?

For coding workloads, Qwen 3.6 27B on NVIDIA A100 80GB receives a S grade with 70.0 tok/s and 262K context.

What context window can Qwen 3.6 27B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 3.6 27B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Qwen 3.6 27B

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<iframe src="https://willitrunai.com/embed/qwen-3.6-27b-on-a100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

70.0 tok/s

1508 ms

262K

Coding

Runs well

70.0 tok/s

2765 ms

262K

Agentic Coding

Runs well

70.0 tok/s

4022 ms

262K

Reasoning

Runs well

70.0 tok/s

3268 ms

262K

RAG

Runs well

70.0 tok/s

5028 ms

262K

Quantization options

How Qwen 3.6 27B (27B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A83
Q3_K_S	3	13.2 GB	Low	A83
NVFP4	4	15.1 GB	Medium	A83
Q4_K_M	4	16.5 GB	Medium	A84
Q5_K_M	5	19.4 GB	High	A84
Q6_K	6	22.1 GB	High	A85
Q8_0	8	28.9 GB	Very High	S86
F16Best for your GPU	16	55.4 GB	Maximum	S90

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

17.7 tok/s

Qwen3-Coder 30B A3B Instruct

30.5B

259 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Qwen 3.6 27B?

Yes, NVIDIA A100 80GB can run Qwen 3.6 27B with a S grade (Runs well). Expected decode speed: 70.0 tok/s.

How much VRAM does Qwen 3.6 27B need?

Qwen 3.6 27B (27B parameters) requires approximately 26.3 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 A100 80GB?

On NVIDIA A100 80GB, Qwen 3.6 27B achieves approximately 70.0 tokens per second decode speed with a time-to-first-token of 2765ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Qwen 3.6 27B for coding?

For coding workloads, Qwen 3.6 27B on NVIDIA A100 80GB receives a S grade with 70.0 tok/s and 262K context.

What context window can Qwen 3.6 27B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 3.6 27B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.