Can GPT-OSS 20B run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

S85Excellent

Estimated from fit model

GPT-OSS 20B needs ~30.6 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~774 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 30.6 GB, 774.0 tok/s, Runs well

30.6 GB required141.0 GB available

22% VRAM used

Fit status

Runs well

Decode

774.0 tok/s

TTFT

350 ms

Safe context

128K

Memory

30.6 GB / 141.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsGPT-OSS 20B 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: 774.0 tok/s decode · 350ms TTFT (warm) · 1935 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	A	Runs well	774.0 tok/s	350 ms	128K
Coding	S	Runs well	774.0 tok/s	350 ms	128K
Agentic Coding	S	Runs well	774.0 tok/s	364 ms	128K
Reasoning	S	Runs well	774.0 tok/s	350 ms	128K
RAG	S	Runs well	774.0 tok/s	455 ms	128K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	A76
Q3_K_S	3	10.3 GB	Low	A77
NVFP4	4

Get started

Copy-paste commands to run GPT-OSS 20B on your machine.

Run

ollama run gpt-oss

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
Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run GPT-OSS 20B?

Yes, NVIDIA H200 PCIe 141GB can run GPT-OSS 20B with a S grade (Runs well). Expected decode speed: 774.0 tok/s.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 30.6 GB of memory with Q4_K_M quantization.

What is the best quantization for GPT-OSS 20B?

The recommended quantization for GPT-OSS 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will GPT-OSS 20B run at on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, GPT-OSS 20B achieves approximately 774.0 tokens per second decode speed with a time-to-first-token of 350ms using Q4_K_M quantization.

Can NVIDIA H200 PCIe 141GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on NVIDIA H200 PCIe 141GB receives a S grade with 774.0 tok/s and 128K context.

What context window can GPT-OSS 20B use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, GPT-OSS 20B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA H200 PCIe 141GB See all hardware for GPT-OSS 20B

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