Can Qwen3-Coder 480B A35B Instruct run on Gaudi 3 128GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen3-Coder 480B A35B Instruct needs ~309.4 GB but Gaudi 3 128GB only has 128.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: StandardBottleneck: Memory capacity

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) — 309.4 GB, exceeds 128.0 GB available

309.4 GB required128.0 GB available

242% VRAM needed

181.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.0 tok/s

TTFT

47803 ms

Safe context

Memory

309.4 GB / 128.0 GB

Offload

60%

Memory breakdown

Weights292.8 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3-Coder 480B A35B Instruct 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: 4.0 tok/s decode · 47.8s TTFT (warm) · 10 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 309.4 GB, but this setup only exposes 128.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

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	F	Too heavy	4.0 tok/s	26074 ms	4K
Coding	F	Too heavy	4.0 tok/s	47803 ms	4K
Agentic Coding	F	Too heavy	4.0 tok/s	69532 ms	4K
Reasoning	F	Too heavy	4.0 tok/s	56494 ms	4K
RAG	F	Too heavy	4.0 tok/s	86914 ms	4K

Quantization options

How Qwen3-Coder 480B A35B Instruct (480B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	187.2 GB	Low	F0
Q3_K_S	3	235.2 GB	Low	F0
NVFP4	4	268.8 GB	Medium	F0
Q4_K_M	4	292.8 GB	Medium	F0
Q5_K_M	5	345.6 GB	High	F0
Q6_K	6	393.6 GB	High	F0
Q8_0	8	513.6 GB	Very High	F0
F16	16	984.0 GB	Maximum	F0

Opciones de mejora

Hardware que ejecuta bien Qwen3-Coder 480B A35B Instruct

AMD Instinct MI350X 288GBMejor relación calidad-precio

288 GB VRAM (+160)8000 GB/s (+4300)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.35.3 tok/s decodificación

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

Sube la velocidad estimada de decodificación alrededor de un 782%.

~$8,000 MSRP

Frequently asked questions

Can Gaudi 3 128GB run Qwen3-Coder 480B A35B Instruct?

No, Qwen3-Coder 480B A35B Instruct requires more memory than Gaudi 3 128GB provides.

How much VRAM does Qwen3-Coder 480B A35B Instruct need?

Qwen3-Coder 480B A35B Instruct (480B parameters) requires approximately 309.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder 480B A35B Instruct?

The recommended quantization for Qwen3-Coder 480B A35B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder 480B A35B Instruct run at on Gaudi 3 128GB?

On Gaudi 3 128GB, Qwen3-Coder 480B A35B Instruct achieves approximately 4.0 tokens per second decode speed with a time-to-first-token of 47803ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Qwen3-Coder 480B A35B Instruct for coding?

For coding workloads, Qwen3-Coder 480B A35B Instruct on Gaudi 3 128GB receives a F grade with 4.0 tok/s and 4K context.

What context window can Qwen3-Coder 480B A35B Instruct use on Gaudi 3 128GB?

On Gaudi 3 128GB, Qwen3-Coder 480B A35B Instruct can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3-Coder 480B A35B Instruct feels slow on Gaudi 3 128GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Would CUDA be a better path than Gaudi 3 128GB for Qwen3-Coder 480B A35B Instruct?

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 Qwen3-Coder 480B A35B Instruct

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