Can GPT-OSS 120B run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

S95Excellent

Estimated from fit model

GPT-OSS 120B needs ~91.3 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~61 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) — 91.3 GB, 61.4 tok/s, Runs well

91.3 GB required141.0 GB available

65% VRAM used

Fit status

Runs well

Decode

61.4 tok/s

TTFT

3151 ms

Safe context

131K

Memory

91.3 GB / 141.0 GB

Memory breakdown

Weights71.4 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsGPT-OSS 120B on NVIDIA H200 PCIe 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: 61.4 tok/s decode · 3.2s TTFT (warm) · 154 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	61.4 tok/s	1719 ms	131K
Coding	S	Runs well	61.4 tok/s	3151 ms	131K
Agentic Coding	S	Runs well	61.4 tok/s	4584 ms	131K
Reasoning	S	Runs well	61.4 tok/s	3724 ms	131K
RAG	S	Runs well	61.4 tok/s	5729 ms	131K

Quantization options

How GPT-OSS 120B (117B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	45.6 GB	Low	A84
Q3_K_S	3	57.3 GB	Low	S85
NVFP4	4

Get started

Copy-paste commands to run GPT-OSS 120B on your machine.

Run

ollama run gpt-oss:120b

Your hardware

More models your NVIDIA H200 PCIe 141GB can run

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

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run GPT-OSS 120B?

Yes, NVIDIA H200 PCIe 141GB can run GPT-OSS 120B with a S grade (Runs well). Expected decode speed: 61.4 tok/s.

How much VRAM does GPT-OSS 120B need?

GPT-OSS 120B (117B parameters) requires approximately 91.3 GB of memory with Q4_K_M quantization.

What is the best quantization for GPT-OSS 120B?

The recommended quantization for GPT-OSS 120B is Q4_K_M, which balances quality and memory efficiency.

What speed will GPT-OSS 120B run at on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, GPT-OSS 120B achieves approximately 61.4 tokens per second decode speed with a time-to-first-token of 3151ms using Q4_K_M quantization.

Can NVIDIA H200 PCIe 141GB run GPT-OSS 120B for coding?

For coding workloads, GPT-OSS 120B on NVIDIA H200 PCIe 141GB receives a S grade with 61.4 tok/s and 131K context.

What context window can GPT-OSS 120B use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, GPT-OSS 120B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA H200 PCIe 141GB See all hardware for GPT-OSS 120B

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