Can Mistral Small 4 119B run on NVIDIA B200 180GB?

YES — Runs Great

S94Excellent

Estimated from fit model

Mistral Small 4 119B needs ~96.9 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~293 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) — 96.9 GB, 292.9 tok/s, Runs well

96.9 GB required180.0 GB available

54% VRAM used

Fit status

Runs well

Decode

292.9 tok/s

TTFT

661 ms

Safe context

256K

Memory

96.9 GB / 180.0 GB

Memory breakdown

Weights72.6 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsMistral Small 4 119B on NVIDIA B200 180GB
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: 292.9 tok/s decode · 661ms TTFT (warm) · 732 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	292.9 tok/s	361 ms	256K
Coding	S	Runs well	292.9 tok/s	661 ms	256K
Agentic Coding	S	Runs well	292.9 tok/s	961 ms	256K
Reasoning	S	Runs well	292.9 tok/s	781 ms	256K
RAG	S	Runs well	292.9 tok/s	1202 ms	256K

Inference speed

Mistral Small 4 119B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Mistral Small 4 119B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~38 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	37.6	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	30.8	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	29.3	Offloads
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	22.9	Offloads
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	11.9	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	10.7	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	8.0	Too big
RTX 5090 32GB	32 GB	Q4_K_M	7.9	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	7.5	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	6.8	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	6.8	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	6.4	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	6.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	5.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	4.5	Too big
RTX 3090 24GB	24 GB	Q4_K_M	4.3	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	4.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Mistral Small 4 119B (119B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A82
Q3_K_S	3	58.3 GB	Low	A83
NVFP4	4	66.6 GB	Medium	A84
Q4_K_M	4	72.6 GB	Medium	S85
Q5_K_M	5	85.7 GB	High	S87
Q6_K	6	97.6 GB	High	S88
Q8_0Best for your GPU	8	127.3 GB	Very High	S88
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 B200 180GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	97.4 tok/s
Qwen 3.5 122B A10B	122B	S	270.2 tok/s
DeepSeek V4 Flash	284B	S	144.8 tok/s

Frequently asked questions

Can NVIDIA B200 180GB run Mistral Small 4 119B?

Yes, NVIDIA B200 180GB can run Mistral Small 4 119B with a S grade (Runs well). Expected decode speed: 292.9 tok/s.

How much VRAM does Mistral Small 4 119B need?

Mistral Small 4 119B (119B parameters) requires approximately 96.9 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 B200 180GB?

On NVIDIA B200 180GB, Mistral Small 4 119B achieves approximately 292.9 tokens per second decode speed with a time-to-first-token of 661ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Mistral Small 4 119B for coding?

For coding workloads, Mistral Small 4 119B on NVIDIA B200 180GB receives a S grade with 292.9 tok/s and 256K context.

What context window can Mistral Small 4 119B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Mistral Small 4 119B can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GB See all hardware for Mistral Small 4 119B

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