Can Qwen 3.6 27B run on RTX A4000 16GB?

YES — With NVFP4

A79Great

Estimated from fit model

Qwen 3.6 27B needs ~18.6 GB VRAM. RTX A4000 16GB has 16.0 GB. With NVFP4 quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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 19.9 GB — too much for RTX A4000 16GB (16.0 GB). Runs at NVFP4 (18.6 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.9 GB, exceeds 16.0 GB available

19.9 GB required16.0 GB available

124% VRAM needed

3.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.4 tok/s

TTFT

26264 ms

Safe context

Memory

19.9 GB / 16.0 GB

Offload

20%

Memory breakdown

Weights16.5 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3.6 27B on RTX A4000 16GB
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: 7.4 tok/s decode · 26.3s TTFT (warm) · 18 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 2.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	7.8 tok/s	13598 ms	4K
Coding	F	Too heavy	7.4 tok/s	26264 ms	4K
Agentic Coding	F	Too heavy	6.7 tok/s	42246 ms	4K
Reasoning	F	Too heavy	7.4 tok/s	31039 ms	4K
RAG	F	Too heavy	6.7 tok/s	52807 ms	4K

Quantization options

How Qwen 3.6 27B (27B params) fits at each quantization level on RTX A4000 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	10.5 GB	Low	S93
Q3_K_S	3	13.2 GB	Low	F0
NVFP4	4	15.1 GB	Medium	F0
Q4_K_M	4	16.5 GB	Medium	F0
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 4000 Ada 20GBOpción económica

20 GB VRAM (+4)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.10.1 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,250 MSRP

RTX 3090 24GBMejor relación calidad-precio

24 GB VRAM (+8)936 GB/s (+488)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.19.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBMejora NVIDIA

24 GB VRAM (+8)672 GB/s (+224)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.28.1 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,599 MSRP

Frequently asked questions

Can RTX A4000 16GB run Qwen 3.6 27B?

Yes, RTX A4000 16GB can run Qwen 3.6 27B at NVFP4 quantization (Very compromised (needs ~2.1 GB host RAM)). The recommended Q4_K_M requires 19.9 GB which exceeds available memory, but at NVFP4 it needs only 18.6 GB. Expected decode speed: 9.8 tok/s.

How much VRAM does Qwen 3.6 27B need?

Qwen 3.6 27B (27B parameters) requires approximately 19.9 GB at Q4_K_M quantization. On RTX A4000 16GB, it fits at NVFP4 using 18.6 GB.

What is the best quantization for Qwen 3.6 27B?

The recommended quantization is Q4_K_M, but on RTX A4000 16GB the best fitting quantization is NVFP4, which uses 18.6 GB.

What speed will Qwen 3.6 27B run at on RTX A4000 16GB?

On RTX A4000 16GB, Qwen 3.6 27B achieves approximately 9.8 tokens per second decode speed with a time-to-first-token of 19814ms using NVFP4 quantization.

Can RTX A4000 16GB run Qwen 3.6 27B for coding?

For coding workloads, Qwen 3.6 27B on RTX A4000 16GB receives a F grade with 7.4 tok/s and 4K context.

What context window can Qwen 3.6 27B use on RTX A4000 16GB?

On RTX A4000 16GB, Qwen 3.6 27B can safely use up to 4K tokens of context at NVFP4 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 RTX A4000 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX A4000 16GB See all hardware for Qwen 3.6 27B

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