Can Mistral Small 4 119B run on NVIDIA H200 141GB?

YES — Runs Great

S97Excellent

○Estimated from fit model

Mistral Small 4 119B needs ~93.0 GB VRAM. NVIDIA H200 141GB has 141.0 GB. With Q4_K_M quantization, expect ~176 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) — 93.0 GB, 175.8 tok/s, Runs well

93.0 GB required141.0 GB available

66% VRAM used

Fit status

Runs well

Decode

175.8 tok/s

TTFT

1102 ms

Safe context

159K

Memory

93.0 GB / 141.0 GB

Memory breakdown

Weights72.6 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsMistral Small 4 119B on NVIDIA H200 141GB
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: 175.8 tok/s decode · 1.1s TTFT (warm) · 439 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	175.8 tok/s	601 ms	159K
Coding	S	Runs well	175.8 tok/s	1102 ms	159K
Agentic Coding	S	Runs well	175.8 tok/s	1602 ms	159K
Reasoning	S	Runs well	175.8 tok/s	1302 ms	159K
RAG	S	Runs well	175.8 tok/s	2003 ms	159K

Quantization options

How Mistral Small 4 119B (119B params) fits at each quantization level on NVIDIA H200 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A84
Q3_K_S	3	58.3 GB	Low	S86
NVFP4	4	66.6 GB	Medium	S87
Q4_K_M	4	72.6 GB	Medium	S88
Q5_K_M	5	85.7 GB	High	S88
Q6_KBest for your GPU	6	97.6 GB	High	S88
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 H200 141GB can run

Model	Params	Grade	Decode	Capabilities
👁 Mistral Devstral 2 123B Instruct	123B	S	58.4 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	162.1 tok/s

Frequently asked questions

See all results for NVIDIA H200 141GB See all hardware for Mistral Small 4 119B

URL: https://willitrunai.com/can-run/mistral-small-4-119b-on-h200-141gb

⇱ Mistral Small 4 119B on NVIDIA H200 141GB? YES