Would CUDA be a better path than Gaudi 3 128GB for Mistral Small 3.1 24B?

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 Mistral Small 3.1 24B run on Gaudi 3 128GB?

Q: What should I upgrade first if Mistral Small 3.1 24B 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

A78Great

Estimated from fit model

Mistral Small 3.1 24B needs ~30.8 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~177 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) — 30.8 GB, 190.2 tok/s, Runs well

30.8 GB required128.0 GB available

24% VRAM used

Fit status

Runs well

Decode

190.2 tok/s

TTFT

1018 ms

Safe context

131K

Memory

30.8 GB / 128.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsMistral Small 3.1 24B 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: 190.2 tok/s decode · 1.0s TTFT (warm) · 476 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	190.2 tok/s	555 ms	131K
Coding	A	Runs well	176.9 tok/s	1094 ms	131K
Agentic Coding	A	Runs well	190.2 tok/s	1481 ms	131K
Reasoning	A	Runs well	190.2 tok/s	1203 ms	131K
RAG	A	Runs well	190.2 tok/s	1851 ms	131K

Quantization options

How Mistral Small 3.1 24B (24B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	B70
Q3_K_S	3	11.8 GB	Low	B70
NVFP4	4

Get started

Copy-paste commands to run Mistral Small 3.1 24B on your machine.

Run

ollama run mistral-small:24b

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 Mistral Small 3.1 24B?

Yes, Gaudi 3 128GB can run Mistral Small 3.1 24B with a A grade (Runs well). Expected decode speed: 176.9 tok/s.

How much VRAM does Mistral Small 3.1 24B need?

Mistral Small 3.1 24B (24B parameters) requires approximately 30.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 3.1 24B?

The recommended quantization for Mistral Small 3.1 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.1 24B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, Mistral Small 3.1 24B achieves approximately 176.9 tokens per second decode speed with a time-to-first-token of 1094ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Mistral Small 3.1 24B for coding?

For coding workloads, Mistral Small 3.1 24B on Gaudi 3 128GB receives a A grade with 176.9 tok/s and 131K context.

What context window can Mistral Small 3.1 24B use on Gaudi 3 128GB?

On Gaudi 3 128GB, Mistral Small 3.1 24B 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 Mistral Small 3.1 24B feels slow on Gaudi 3 128GB?

See all results for Gaudi 3 128GB See all hardware for Mistral Small 3.1 24B

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