Can GPT-OSS 20B run on RTX 5000 Ada 32GB?

YES — Runs Great

S94Excellent

Estimated from fit model

GPT-OSS 20B needs ~19.7 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~89 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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, 88.5 tok/s, Runs well

19.7 GB required32.0 GB available

62% VRAM used

Fit status

Runs well

Decode

88.5 tok/s

TTFT

2189 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 RTX 5000 Ada 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: 88.5 tok/s decode · 2.2s TTFT (warm) · 221 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	88.5 tok/s	1194 ms	97K
Coding	S	Runs well	88.5 tok/s	2189 ms	97K
Agentic Coding	S	Runs well	88.5 tok/s	3183 ms	97K
Reasoning	S	Runs well	88.5 tok/s	2586 ms	97K
RAG	S	Runs well	88.5 tok/s	3979 ms	97K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on RTX 5000 Ada 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	11.8 GB	Medium	S85
Q4_K_M	4	12.8 GB	Medium	S86
Q5_K_M	5	15.1 GB	High	S87
Q6_K	6	17.2 GB	High	S88
Q8_0Best for your GPU	8	22.5 GB	Very High	S87
F16	16	43.1 GB	Maximum	F0

Get started

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

Run

ollama run gpt-oss

Your hardware

More models your RTX 5000 Ada 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	69.7 tok/s
Qwen 3.5 27B	27B	S	30.2 tok/s
Qwen 3.6 27B	27B	S	30.3 tok/s
Qwen 3.6 35B A3B	35B	S	58.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	72.1 tok/s

Frequently asked questions

Can RTX 5000 Ada 32GB run GPT-OSS 20B?

Yes, RTX 5000 Ada 32GB can run GPT-OSS 20B with a S grade (Runs well). Expected decode speed: 88.5 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 RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, GPT-OSS 20B achieves approximately 88.5 tokens per second decode speed with a time-to-first-token of 2189ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on RTX 5000 Ada 32GB receives a S grade with 88.5 tok/s and 97K context.

What context window can GPT-OSS 20B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 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 RTX 5000 Ada 32GB See all hardware for GPT-OSS 20B

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<iframe src="https://willitrunai.com/embed/gpt-oss-20b-on-rtx-5000-ada-32gb" 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

88.5 tok/s

1194 ms

97K

Coding

Runs well

88.5 tok/s

2189 ms

97K

Agentic Coding

Runs well

88.5 tok/s

3183 ms

97K

Reasoning

Runs well

88.5 tok/s

2586 ms

97K

RAG

Runs well

88.5 tok/s

3979 ms

97K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on RTX 5000 Ada 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	11.8 GB	Medium	S85
Q4_K_M	4	12.8 GB	Medium	S86
Q5_K_M	5	15.1 GB	High	S87
Q6_K	6	17.2 GB	High	S88
Q8_0Best for your GPU	8	22.5 GB	Very High	S87
F16	16	43.1 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

69.7 tok/s

Qwen 3.5 27B

27B

30.2 tok/s

Qwen 3.6 27B

27B

30.3 tok/s

Qwen 3.6 35B A3B

35B

58.6 tok/s

Qwen3-VL 30B A3B Instruct

30B

72.1 tok/s

Frequently asked questions

Can RTX 5000 Ada 32GB run GPT-OSS 20B?

Yes, RTX 5000 Ada 32GB can run GPT-OSS 20B with a S grade (Runs well). Expected decode speed: 88.5 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 RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, GPT-OSS 20B achieves approximately 88.5 tokens per second decode speed with a time-to-first-token of 2189ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on RTX 5000 Ada 32GB receives a S grade with 88.5 tok/s and 97K context.

What context window can GPT-OSS 20B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 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.