Can Qwen 3.6 27B run on Gaudi 3 128GB?

YES — Runs Great

S90Excellent

Estimated from fit model

Qwen 3.6 27B needs ~31.1 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~106 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) — 31.1 GB, 105.9 tok/s, Runs well

31.1 GB required128.0 GB available

24% VRAM used

Fit status

Runs well

Decode

105.9 tok/s

TTFT

1829 ms

Safe context

262K

Memory

31.1 GB / 128.0 GB

Memory breakdown

Weights16.5 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsQwen 3.6 27B on Gaudi 3 128GB
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: 105.9 tok/s decode · 1.8s TTFT (warm) · 265 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	105.9 tok/s	997 ms	262K
Coding	S	Runs well	105.9 tok/s	1829 ms	262K
Agentic Coding	S	Runs well	105.9 tok/s	2660 ms	262K
Reasoning	S	Runs well	105.9 tok/s	2161 ms	262K
RAG	S	Runs well	105.9 tok/s	3325 ms	262K

Quantization options

How Qwen 3.6 27B (27B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A81
Q3_K_S	3	13.2 GB	Low	A81
NVFP4	4	15.1 GB	Medium	A81
Q4_K_M	4	16.5 GB	Medium	A81
Q5_K_M	5	19.4 GB	High	A81
Q6_K	6	22.1 GB	High	A82
Q8_0	8	28.9 GB	Very High	A83
F16Best for your GPU	16	55.4 GB	Maximum	S87

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 Gaudi 3 128GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	37.5 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	391.6 tok/s

Frequently asked questions

Can Gaudi 3 128GB run Qwen 3.6 27B?

Yes, Gaudi 3 128GB can run Qwen 3.6 27B with a S grade (Runs well). Expected decode speed: 105.9 tok/s.

How much VRAM does Qwen 3.6 27B need?

Qwen 3.6 27B (27B parameters) requires approximately 31.1 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 Gaudi 3 128GB?

On Gaudi 3 128GB, Qwen 3.6 27B achieves approximately 105.9 tokens per second decode speed with a time-to-first-token of 1829ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Qwen 3.6 27B for coding?

For coding workloads, Qwen 3.6 27B on Gaudi 3 128GB receives a S grade with 105.9 tok/s and 262K context.

What context window can Qwen 3.6 27B use on Gaudi 3 128GB?

On Gaudi 3 128GB, 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.

What should I upgrade first if Qwen 3.6 27B feels slow on Gaudi 3 128GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Gaudi 3 128GB for Qwen 3.6 27B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Gaudi 3 128GB See all hardware for Qwen 3.6 27B

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