Can Qwen 3.6 27B run on RTX 4090 24GB?

YES — Tight Fit

S94Excellent

Estimated from fit model

Qwen 3.6 27B needs ~22.4 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~50 tok/s.

Runtime: SGLangCapacity: TightBandwidth: HighStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.4 GB, 50.4 tok/s, Tight fit

22.4 GB required24.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

50.4 tok/s

TTFT

3842 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 RTX 4090 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: 50.4 tok/s decode · 3.8s TTFT (warm) · 126 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	S	Tight fit	50.4 tok/s	2096 ms	41K
Coding	S	Tight fit	50.4 tok/s	3842 ms	41K
Agentic Coding	F	Too heavy	50.4 tok/s	5588 ms	41K
Reasoning	S	Tight fit	50.4 tok/s	4541 ms	41K
RAG	F	Too heavy	50.4 tok/s	6986 ms	41K

Quantization options

How Qwen 3.6 27B (27B params) fits at each quantization level on RTX 4090 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

Get started

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

Run

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

Frequently asked questions

Can RTX 4090 24GB run Qwen 3.6 27B?

Yes, RTX 4090 24GB can run Qwen 3.6 27B with a S grade (Tight fit). Expected decode speed: 50.4 tok/s.

How much VRAM does Qwen 3.6 27B need?

Qwen 3.6 27B (27B parameters) requires approximately 22.4 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 RTX 4090 24GB?

On RTX 4090 24GB, Qwen 3.6 27B achieves approximately 50.4 tokens per second decode speed with a time-to-first-token of 3842ms using Q4_K_M quantization.

Can RTX 4090 24GB run Qwen 3.6 27B for coding?

For coding workloads, Qwen 3.6 27B on RTX 4090 24GB receives a S grade with 50.4 tok/s and 41K context.

What context window can Qwen 3.6 27B use on RTX 4090 24GB?

On RTX 4090 24GB, Qwen 3.6 27B can safely use up to 41K tokens of context. 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 RTX 4090 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 RTX 4090 24GB See all hardware for Qwen 3.6 27B

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