Can Qwen 3.6 27B run on NVIDIA L4 24GB?

YES — With Q4_K_M

S90Excellent

Estimated from fit model

Qwen 3.6 27B needs ~22.4 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: SGLangCapacity: TightBandwidth: LowStack: OptimizedBottleneck: 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.

Qwen 3.6 27B at Q4_K_M needs 25.4 GB — too much for NVIDIA L4 24GB (24.0 GB). Runs at Q4_K_M (22.4 GB) with medium quality. 4 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.4 GB, 12.8 tok/s, Tight fit

22.4 GB required24.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

12.8 tok/s

TTFT

15094 ms

Safe context

41K

Memory

22.4 GB / 24.0 GB

Memory breakdown

Weights16.5 GB

KV Cache1.0 GB

Runtime2.6 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 3.6 27B on NVIDIA L4 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: 12.8 tok/s decode · 15.1s TTFT (warm) · 32 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	11.8 tok/s	8919 ms	10K
Coding	F	Too heavy	7.9 tok/s	24518 ms	10K
Agentic Coding	F	Too heavy	5.8 tok/s	48205 ms	10K
Reasoning	F	Too heavy	7.9 tok/s	28975 ms	10K
RAG	F	Too heavy	5.8 tok/s	60257 ms	10K

Quantization options

How Qwen 3.6 27B (27B params) fits at each quantization level on NVIDIA L4 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

Opciones de mejora

Hardware que ejecuta bien Qwen 3.6 27B

RTX 5090 32GBOpción económica

32 GB VRAM (+8)1792 GB/s (+1492)

Sube la velocidad estimada de decodificación alrededor de un 517%.79 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 517%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMejor relación calidad-precio

32 GB VRAM (+8)896 GB/s (+596)

Sube la velocidad estimada de decodificación alrededor de un 287%.49.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 287%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

RTX 5000 Ada 32GBMejora NVIDIA

32 GB VRAM (+8)576 GB/s (+276)

Sube la velocidad estimada de decodificación alrededor de un 137%.30.3 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 137%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Qwen 3.6 27B?

Yes, NVIDIA L4 24GB can run Qwen 3.6 27B at Q4_K_M quantization (Tight fit). The recommended Q4_K_M requires 25.4 GB which exceeds available memory, but at Q4_K_M it needs only 22.4 GB. Expected decode speed: 12.8 tok/s.

How much VRAM does Qwen 3.6 27B need?

Qwen 3.6 27B (27B parameters) requires approximately 25.4 GB at Q4_K_M quantization. On NVIDIA L4 24GB, it fits at Q4_K_M using 22.4 GB.

What is the best quantization for Qwen 3.6 27B?

The recommended quantization is Q4_K_M, but on NVIDIA L4 24GB the best fitting quantization is Q4_K_M, which uses 22.4 GB.

What speed will Qwen 3.6 27B run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Qwen 3.6 27B achieves approximately 12.8 tokens per second decode speed with a time-to-first-token of 15094ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Qwen 3.6 27B for coding?

For coding workloads, Qwen 3.6 27B on NVIDIA L4 24GB receives a F grade with 7.9 tok/s and 10K context.

What context window can Qwen 3.6 27B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Qwen 3.6 27B can safely use up to 41K tokens of context at Q4_K_M quantization. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3.6 27B feels slow on NVIDIA L4 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for NVIDIA L4 24GB See all hardware for Qwen 3.6 27B

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