Would CUDA be a better path than Gaudi 3 128GB for DeepSeek V3 671B?

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 DeepSeek V3 671B run on Gaudi 3 128GB?

Q: What should I upgrade first if DeepSeek V3 671B 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.

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek V3 671B needs ~482.6 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) — 482.6 GB, exceeds 128.0 GB available

482.6 GB required128.0 GB available

377% VRAM needed

354.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.8 tok/s

TTFT

70189 ms

Safe context

Memory

482.6 GB / 128.0 GB

Offload

70%

Memory breakdown

Weights409.3 GB

KV Cache59.6 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek V3 671B 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: 2.8 tok/s decode · 70.2s TTFT (warm) · 7 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 482.6 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	2.8 tok/s	38285 ms	4K
Coding	F	Too heavy	2.8 tok/s	70189 ms	4K
Agentic Coding	F	Too heavy	2.8 tok/s	102093 ms	4K
Reasoning	F	Too heavy	2.8 tok/s	82950 ms	4K
RAG	F	Too heavy	2.8 tok/s	127616 ms	4K

Quantization options

How DeepSeek V3 671B (671B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	261.7 GB	Low	F0
Q3_K_S	3	328.8 GB	Low	F0
NVFP4	4

Frequently asked questions

Can Gaudi 3 128GB run DeepSeek V3 671B?

No, DeepSeek V3 671B requires more memory than Gaudi 3 128GB provides.

How much VRAM does DeepSeek V3 671B need?

DeepSeek V3 671B (671B parameters) requires approximately 482.6 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek V3 671B?

The recommended quantization for DeepSeek V3 671B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek V3 671B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, DeepSeek V3 671B achieves approximately 2.8 tokens per second decode speed with a time-to-first-token of 70189ms using Q4_K_M quantization.

Can Gaudi 3 128GB run DeepSeek V3 671B for coding?

For coding workloads, DeepSeek V3 671B on Gaudi 3 128GB receives a F grade with 2.8 tok/s and 4K context.

What context window can DeepSeek V3 671B use on Gaudi 3 128GB?

On Gaudi 3 128GB, DeepSeek V3 671B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek V3 671B feels slow on Gaudi 3 128GB?

See all results for Gaudi 3 128GB See all hardware for DeepSeek V3 671B

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