Can Qwen 3.6 27B run on AMD Instinct MI350X 288GB?

YES — Runs Great

S88Excellent

Estimated from fit model

Qwen 3.6 27B needs ~50.1 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~220 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 47.1 GB, 238.7 tok/s, Runs well

47.1 GB required288.0 GB available

16% VRAM used

Fit status

Runs well

Decode

238.7 tok/s

TTFT

811 ms

Safe context

262K

Memory

47.1 GB / 288.0 GB

Memory breakdown

Weights16.5 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

See how fast it feelsQwen 3.6 27B on AMD Instinct MI350X 288GB
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: 238.7 tok/s decode · 811ms TTFT (warm) · 597 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	238.7 tok/s	442 ms	262K
Coding	S	Runs well	220.4 tok/s	879 ms	262K
Agentic Coding	S	Runs well	238.7 tok/s	1180 ms	262K
Reasoning	S	Runs well	238.7 tok/s	958 ms	262K
RAG	S	Runs well	238.7 tok/s	1475 ms	262K

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

Quantization options

How Qwen 3.6 27B (27B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A78
Q3_K_S	3	13.2 GB	Low	A79
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

Your hardware

More models your AMD Instinct MI350X 288GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 397B A17B	397B	S	78.9 tok/s
Devstral 2 123B Instruct	123B	S

Frequently asked questions

Can AMD Instinct MI350X 288GB run Qwen 3.6 27B?

Yes, AMD Instinct MI350X 288GB can run Qwen 3.6 27B with a S grade (Runs well). Expected decode speed: 220.4 tok/s.

How much VRAM does Qwen 3.6 27B need?

Qwen 3.6 27B (27B parameters) requires approximately 50.1 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 AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Qwen 3.6 27B achieves approximately 220.4 tokens per second decode speed with a time-to-first-token of 879ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run Qwen 3.6 27B for coding?

For coding workloads, Qwen 3.6 27B on AMD Instinct MI350X 288GB receives a S grade with 220.4 tok/s and 262K context.

What context window can Qwen 3.6 27B use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Qwen 3.6 27B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI350X 288GB See all hardware for Qwen 3.6 27B

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