Can Nemotron 3 Nano 30B run on Gaudi 3 128GB?

YES — Runs Great

S88Excellent

Estimated from fit model

Nemotron 3 Nano 30B needs ~34.4 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~152 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) — 34.4 GB, 152.1 tok/s, Runs well

34.4 GB required128.0 GB available

27% VRAM used

Fit status

Runs well

Decode

152.1 tok/s

TTFT

1272 ms

Safe context

131K

Memory

34.4 GB / 128.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsNemotron 3 Nano 30B 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: 152.1 tok/s decode · 1.3s TTFT (warm) · 380 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	152.1 tok/s	694 ms	131K
Coding	S	Runs well	152.1 tok/s	1272 ms	131K
Agentic Coding	S	Runs well	152.1 tok/s	1851 ms	131K
Reasoning	S	Runs well	152.1 tok/s	1504 ms	131K
RAG	S	Runs well	152.1 tok/s	2314 ms	131K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A79
Q3_K_S	3	14.7 GB	Low	A79
NVFP4	4	16.8 GB	Medium	A79
Q4_K_M	4	18.3 GB	Medium	A79
Q5_K_M	5	21.6 GB	High	A79
Q6_K	6	24.6 GB	High	A80
Q8_0	8	32.1 GB	Very High	A81
F16Best for your GPU	16	61.5 GB	Maximum	S86

Get started

Copy-paste commands to run Nemotron 3 Nano 30B on your machine.

Run

ollama run nemotron-nano:30b

Your hardware

More models your Gaudi 3 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	37.5 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	391.6 tok/s
Qwen 3.5 122B A10B	122B	S	104.1 tok/s
Qwen 3.6 35B A3B	35B	S	329.1 tok/s
Qwen 3.5 35B A3B	35B	S	357.9 tok/s

Frequently asked questions

Can Gaudi 3 128GB run Nemotron 3 Nano 30B?

Yes, Gaudi 3 128GB can run Nemotron 3 Nano 30B with a S grade (Runs well). Expected decode speed: 152.1 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 34.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron 3 Nano 30B?

The recommended quantization for Nemotron 3 Nano 30B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron 3 Nano 30B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, Nemotron 3 Nano 30B achieves approximately 152.1 tokens per second decode speed with a time-to-first-token of 1272ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on Gaudi 3 128GB receives a S grade with 152.1 tok/s and 131K context.

What context window can Nemotron 3 Nano 30B use on Gaudi 3 128GB?

On Gaudi 3 128GB, Nemotron 3 Nano 30B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Nemotron 3 Nano 30B 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 Nemotron 3 Nano 30B?

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 Nemotron 3 Nano 30B

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