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

Inference speed

Qwen 3.6 27B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.6 27B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~79 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	79.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	50.4	Tight
RTX 3090 24GB	24 GB	Q4_K_M	43.1	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	29.8	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	27.8	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	27.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	27.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	23.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	21.9	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	17.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	12.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	11.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	9.1	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.2	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

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	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

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

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-3.6-27b-on-rtx-4090-24gb" 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

Tight fit

50.4 tok/s

2096 ms

41K

Coding

Tight fit

50.4 tok/s

3842 ms

41K

Agentic Coding

Too heavy

50.4 tok/s

5588 ms

41K

Reasoning

Tight fit

50.4 tok/s

4541 ms

41K

RAG

Too heavy

50.4 tok/s

6986 ms

41K

Qwen 3.6 27B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

79.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

50.4

Tight

RTX 3090 24GB

24 GB

Q4_K_M

43.1

Tight

RX 7900 XTX 24GB

24 GB

Q4_K_M

29.8

Tight

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

27.8

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

27.4

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

27.4

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

23.1

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

21.9

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

17.3

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

12.0

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

11.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

9.1

Too big

RTX 4070 12GB

12 GB

Q4_K_M

3.2

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.2

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

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	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

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.