Can Qwen 3.6 27B run on Tesla P40 24GB?

YES — Tight Fit

S89Excellent

Estimated from fit model

Qwen 3.6 27B needs ~20.7 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 20.7 GB, 10.2 tok/s, Tight fit

20.7 GB required24.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

10.2 tok/s

TTFT

19025 ms

Safe context

69K

Memory

20.7 GB / 24.0 GB

Memory breakdown

Weights16.5 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 3.6 27B on Tesla P40 24GB
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: 10.2 tok/s decode · 19.0s TTFT (warm) · 25 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	10.2 tok/s	10377 ms	69K
Coding	S	Tight fit	10.2 tok/s	19025 ms	69K
Agentic Coding	S	Tight fit	10.2 tok/s	27672 ms	69K
Reasoning	S	Tight fit	10.2 tok/s	22484 ms	69K
RAG	S	Tight fit	10.2 tok/s	34591 ms	69K

Quantization options

How Qwen 3.6 27B (27B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	S92
Q3_K_S	3	13.2 GB	Low	S93
NVFP4	4	15.1 GB	Medium	S92
Q4_K_MBest for your GPU	4	16.5 GB	Medium	S92
Q5_K_M	5	19.4 GB	High	F0
Q6_K	6	22.1 GB	High	F0
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 Tesla P40 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	30.9 tok/s

Frequently asked questions

Can Tesla P40 24GB run Qwen 3.6 27B?

Yes, Tesla P40 24GB can run Qwen 3.6 27B with a S grade (Tight fit). Expected decode speed: 10.2 tok/s.

How much VRAM does Qwen 3.6 27B need?

Qwen 3.6 27B (27B parameters) requires approximately 20.7 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 Tesla P40 24GB?

On Tesla P40 24GB, Qwen 3.6 27B achieves approximately 10.2 tokens per second decode speed with a time-to-first-token of 19025ms using Q4_K_M quantization.

Can Tesla P40 24GB run Qwen 3.6 27B for coding?

For coding workloads, Qwen 3.6 27B on Tesla P40 24GB receives a S grade with 10.2 tok/s and 69K context.

What context window can Qwen 3.6 27B use on Tesla P40 24GB?

On Tesla P40 24GB, Qwen 3.6 27B can safely use up to 69K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for Qwen 3.6 27B

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