Would CUDA be a better path than Gaudi 3 128GB for Qwen 3.6 35B A3B?

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.

Can Qwen 3.6 35B A3B run on Gaudi 3 128GB?

Q: What should I upgrade first if Qwen 3.6 35B A3B 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.

YES — Runs Great

S91Excellent

Estimated from fit model

Qwen 3.6 35B A3B needs ~40.1 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~329 tok/s.

Runtime: TransformersCapacity: 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) — 40.1 GB, 329.1 tok/s, Runs well

40.1 GB required128.0 GB available

31% VRAM used

Fit status

Runs well

Decode

329.1 tok/s

TTFT

588 ms

Safe context

262K

Memory

40.1 GB / 128.0 GB

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime1.8 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsQwen 3.6 35B A3B 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: 329.1 tok/s decode · 588ms TTFT (warm) · 823 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	329.1 tok/s	350 ms	262K
Coding	S	Runs well	329.1 tok/s	588 ms	262K
Agentic Coding	S	Runs well	329.1 tok/s	856 ms	262K
Reasoning	S	Runs well	329.1 tok/s	695 ms	262K
RAG	S	Runs well	329.1 tok/s	1069 ms	262K

Quantization options

How Qwen 3.6 35B A3B (35B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A81
Q3_K_S	3	17.2 GB	Low	A81
NVFP4	4

Get started

Copy-paste commands to run Qwen 3.6 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "Qwen/Qwen3.6-35B-A3B" \
  --hf-file "Qwen3.6-35B-A3B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

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
Qwen 3.5 122B A10B	122B	S

Frequently asked questions

Can Gaudi 3 128GB run Qwen 3.6 35B A3B?

Yes, Gaudi 3 128GB can run Qwen 3.6 35B A3B with a S grade (Runs well). Expected decode speed: 329.1 tok/s.

How much VRAM does Qwen 3.6 35B A3B need?

Qwen 3.6 35B A3B (35B parameters) requires approximately 40.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.6 35B A3B?

The recommended quantization for Qwen 3.6 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.6 35B A3B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, Qwen 3.6 35B A3B achieves approximately 329.1 tokens per second decode speed with a time-to-first-token of 588ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Qwen 3.6 35B A3B for coding?

For coding workloads, Qwen 3.6 35B A3B on Gaudi 3 128GB receives a S grade with 329.1 tok/s and 262K context.

What context window can Qwen 3.6 35B A3B use on Gaudi 3 128GB?

On Gaudi 3 128GB, Qwen 3.6 35B A3B 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 35B A3B feels slow on Gaudi 3 128GB?

See all results for Gaudi 3 128GB See all hardware for Qwen 3.6 35B A3B

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