Can GPT-OSS 20B run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

S87Excellent

Estimated from fit model

GPT-OSS 20B needs ~24.5 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~323 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) — 24.5 GB, 322.5 tok/s, Runs well

24.5 GB required80.0 GB available

31% VRAM used

Fit status

Runs well

Decode

322.5 tok/s

TTFT

600 ms

Safe context

128K

Memory

24.5 GB / 80.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsGPT-OSS 20B on NVIDIA H100 PCIe 80GB
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: 322.5 tok/s decode · 600ms TTFT (warm) · 806 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	322.5 tok/s	350 ms	128K
Coding	S	Runs well	322.5 tok/s	600 ms	128K
Agentic Coding	S	Runs well	322.5 tok/s	873 ms	128K
Reasoning	S	Runs well	322.5 tok/s	709 ms	128K
RAG	S	Runs well	322.5 tok/s	1091 ms	128K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	A79
Q3_K_S	3	10.3 GB	Low	A79
NVFP4	4	11.8 GB	Medium	A79
Q4_K_M	4	12.8 GB	Medium	A79
Q5_K_M	5	15.1 GB	High	A79
Q6_K	6	17.2 GB	High	A80
Q8_0	8	22.5 GB	Very High	A81
F16Best for your GPU	16	43.1 GB	Maximum	S86

Get started

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

Run

ollama run gpt-oss

Your hardware

More models your NVIDIA H100 PCIe 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	14.8 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	254 tok/s
Qwen 3.5 27B	27B	S	110.2 tok/s
Qwen 3.6 27B	27B	S	110.5 tok/s
Qwen 3.5 122B A10B	122B	A	44.5 tok/s

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run GPT-OSS 20B?

Yes, NVIDIA H100 PCIe 80GB can run GPT-OSS 20B with a S grade (Runs well). Expected decode speed: 322.5 tok/s.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 24.5 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 H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, GPT-OSS 20B achieves approximately 322.5 tokens per second decode speed with a time-to-first-token of 600ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on NVIDIA H100 PCIe 80GB receives a S grade with 322.5 tok/s and 128K context.

What context window can GPT-OSS 20B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, 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 H100 PCIe 80GB See all hardware for GPT-OSS 20B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/gpt-oss-20b-on-h100-pcie-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

322.5 tok/s

350 ms

128K

Coding

Runs well

322.5 tok/s

600 ms

128K

Agentic Coding

Runs well

322.5 tok/s

873 ms

128K

Reasoning

Runs well

322.5 tok/s

709 ms

128K

RAG

Runs well

322.5 tok/s

1091 ms

128K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	A79
Q3_K_S	3	10.3 GB	Low	A79
NVFP4	4	11.8 GB	Medium	A79
Q4_K_M	4	12.8 GB	Medium	A79
Q5_K_M	5	15.1 GB	High	A79
Q6_K	6	17.2 GB	High	A80
Q8_0	8	22.5 GB	Very High	A81
F16Best for your GPU	16	43.1 GB	Maximum	S86

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

14.8 tok/s

Qwen3-Coder 30B A3B Instruct

30.5B

254 tok/s

Qwen 3.5 27B

27B

110.2 tok/s

Qwen 3.6 27B

27B

110.5 tok/s

Qwen 3.5 122B A10B

122B

44.5 tok/s

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run GPT-OSS 20B?

Yes, NVIDIA H100 PCIe 80GB can run GPT-OSS 20B with a S grade (Runs well). Expected decode speed: 322.5 tok/s.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 24.5 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 H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, GPT-OSS 20B achieves approximately 322.5 tokens per second decode speed with a time-to-first-token of 600ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on NVIDIA H100 PCIe 80GB receives a S grade with 322.5 tok/s and 128K context.

What context window can GPT-OSS 20B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, 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.