Would CUDA be a better path than Gaudi 3 128GB for Mistral Small 4 119B?

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 4 119B run on Gaudi 3 128GB?

Q: What should I upgrade first if Mistral Small 4 119B 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

S97Excellent

Estimated from fit model

Mistral Small 4 119B needs ~91.7 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~113 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) — 91.7 GB, 112.9 tok/s, Runs well

91.7 GB required128.0 GB available

72% VRAM used

Fit status

Runs well

Decode

112.9 tok/s

TTFT

1715 ms

Safe context

124K

Memory

91.7 GB / 128.0 GB

Memory breakdown

Weights72.6 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsMistral Small 4 119B 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: 112.9 tok/s decode · 1.7s TTFT (warm) · 282 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	112.9 tok/s	935 ms	124K
Coding	S	Runs well	112.9 tok/s	1715 ms	124K
Agentic Coding	S	Runs well	112.9 tok/s	2494 ms	124K
Reasoning	S	Runs well	112.9 tok/s	2027 ms	124K
RAG	S	Runs well	112.9 tok/s	3118 ms	124K

Quantization options

How Mistral Small 4 119B (119B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A85
Q3_K_S	3	58.3 GB	Low	S87
NVFP4	4

Get started

Copy-paste commands to run Mistral Small 4 119B on your machine.

Run

lms load Mistral-Small-4-119B-2603 && lms server start

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
Qwen 3.5 122B A10B	122B	S

Frequently asked questions

Can Gaudi 3 128GB run Mistral Small 4 119B?

Yes, Gaudi 3 128GB can run Mistral Small 4 119B with a S grade (Runs well). Expected decode speed: 112.9 tok/s.

How much VRAM does Mistral Small 4 119B need?

Mistral Small 4 119B (119B parameters) requires approximately 91.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 4 119B?

The recommended quantization for Mistral Small 4 119B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 4 119B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, Mistral Small 4 119B achieves approximately 112.9 tokens per second decode speed with a time-to-first-token of 1715ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Mistral Small 4 119B for coding?

For coding workloads, Mistral Small 4 119B on Gaudi 3 128GB receives a S grade with 112.9 tok/s and 124K context.

What context window can Mistral Small 4 119B use on Gaudi 3 128GB?

On Gaudi 3 128GB, Mistral Small 4 119B can safely use up to 124K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Small 4 119B feels slow on Gaudi 3 128GB?

See all results for Gaudi 3 128GB See all hardware for Mistral Small 4 119B

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