Can Mistral Small 4 119B run on NVIDIA GH200 96GB?

YES — Tight Fit

S94Excellent

Estimated from fit model

Mistral Small 4 119B needs ~88.5 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q4_K_M quantization, expect ~141 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 88.5 GB, 141.2 tok/s, Tight fit

88.5 GB required96.0 GB available

92% VRAM used

Fit status

Tight fit

Decode

141.2 tok/s

TTFT

1371 ms

Safe context

38K

Memory

88.5 GB / 96.0 GB

Memory breakdown

Weights72.6 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsMistral Small 4 119B on NVIDIA GH200 96GB
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: 141.2 tok/s decode · 1.4s TTFT (warm) · 353 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	141.2 tok/s	748 ms	38K
Coding	S	Tight fit	141.2 tok/s	1371 ms	38K
Agentic Coding	S	Runs with offload	129.9 tok/s	2168 ms	38K
Reasoning	S	Tight fit	141.2 tok/s	1620 ms	38K
RAG	S	Runs with offload	141.2 tok/s	2492 ms	38K

Quantization options

How Mistral Small 4 119B (119B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	S88
Q3_K_S	3	58.3 GB	Low	S88
NVFP4	4	66.6 GB	Medium	S88
Q4_K_MBest for your GPU	4	72.6 GB	Medium	S88
Q5_K_M	5	85.7 GB	High	F0
Q6_K	6	97.6 GB	High	F0
Q8_0	8	127.3 GB	Very High	F0
F16	16	244.0 GB	Maximum	F0

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 NVIDIA GH200 96GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	47 tok/s
Qwen 3.5 122B A10B	122B	S	130.3 tok/s

Frequently asked questions

Can NVIDIA GH200 96GB run Mistral Small 4 119B?

Yes, NVIDIA GH200 96GB can run Mistral Small 4 119B with a S grade (Tight fit). Expected decode speed: 141.2 tok/s.

How much VRAM does Mistral Small 4 119B need?

Mistral Small 4 119B (119B parameters) requires approximately 88.5 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 NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, Mistral Small 4 119B achieves approximately 141.2 tokens per second decode speed with a time-to-first-token of 1371ms using Q4_K_M quantization.

Can NVIDIA GH200 96GB run Mistral Small 4 119B for coding?

For coding workloads, Mistral Small 4 119B on NVIDIA GH200 96GB receives a S grade with 141.2 tok/s and 38K context.

What context window can Mistral Small 4 119B use on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, Mistral Small 4 119B can safely use up to 38K 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 NVIDIA GH200 96GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for NVIDIA GH200 96GB See all hardware for Mistral Small 4 119B

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