Would CUDA be a better path than Gaudi 3 128GB for Gemma 3 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.

Can Gemma 3 27B run on Gaudi 3 128GB?

YES — Runs Great

A81Great

Estimated from fit model

Gemma 3 27B needs ~41.4 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~98 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) — 41.4 GB, 102.6 tok/s, Runs well

41.4 GB required128.0 GB available

32% VRAM used

Fit status

Runs well

Decode

102.6 tok/s

TTFT

1887 ms

Safe context

131K

Memory

41.4 GB / 128.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsGemma 3 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: 102.6 tok/s decode · 1.9s TTFT (warm) · 257 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	A	Runs well	97.7 tok/s	1081 ms	131K
Coding	A	Runs well	97.7 tok/s	1981 ms	131K
Agentic Coding	A	Runs well	97.7 tok/s	2882 ms	131K
Reasoning	A	Runs well	97.7 tok/s	2341 ms	131K
RAG	A	Runs well	97.7 tok/s	3602 ms	131K

Quantization options

How Gemma 3 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	A71
Q3_K_S	3	13.2 GB	Low	A71
NVFP4	4

Get started

Copy-paste commands to run Gemma 3 27B on your machine.

Run

ollama run gemma3

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

Frequently asked questions

Can Gaudi 3 128GB run Gemma 3 27B?

Yes, Gaudi 3 128GB can run Gemma 3 27B with a A grade (Runs well). Expected decode speed: 97.7 tok/s.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 41.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 27B?

The recommended quantization for Gemma 3 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 27B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, Gemma 3 27B achieves approximately 97.7 tokens per second decode speed with a time-to-first-token of 1981ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on Gaudi 3 128GB receives a A grade with 97.7 tok/s and 131K context.

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

On Gaudi 3 128GB, Gemma 3 27B 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 Gemma 3 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.

See all results for Gaudi 3 128GB See all hardware for Gemma 3 27B

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