Can GPT-OSS 20B run on NVIDIA V100 32GB?

YES — Runs Great

S94Excellent

Estimated from fit model

GPT-OSS 20B needs ~19.7 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~116 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) — 19.7 GB, 115.8 tok/s, Runs well

19.7 GB required32.0 GB available

62% VRAM used

Fit status

Runs well

Decode

115.8 tok/s

TTFT

1672 ms

Safe context

97K

Memory

19.7 GB / 32.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGPT-OSS 20B on NVIDIA V100 32GB
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: 115.8 tok/s decode · 1.7s TTFT (warm) · 289 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	115.8 tok/s	912 ms	97K
Coding	S	Runs well	115.8 tok/s	1672 ms	97K
Agentic Coding	S	Runs well	115.8 tok/s	2433 ms	97K
Reasoning	S	Runs well	115.8 tok/s	1977 ms	97K
RAG	S	Runs well	107.7 tok/s	3269 ms	97K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	A84
Q3_K_S	3	10.3 GB	Low	A85
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 V100 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	91.2 tok/s
Qwen 3.5 27B	27B	S	39.5 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run GPT-OSS 20B?

Yes, NVIDIA V100 32GB can run GPT-OSS 20B with a S grade (Runs well). Expected decode speed: 115.8 tok/s.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 19.7 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 V100 32GB?

On NVIDIA V100 32GB, GPT-OSS 20B achieves approximately 115.8 tokens per second decode speed with a time-to-first-token of 1672ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on NVIDIA V100 32GB receives a S grade with 115.8 tok/s and 97K context.

What context window can GPT-OSS 20B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, GPT-OSS 20B can safely use up to 97K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for GPT-OSS 20B

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