Can GPT-OSS 20B run on RTX 4090 24GB?

YES — Runs Great

S95Excellent

Estimated — low-sample bucket· few comparable runs

GPT-OSS 20B needs ~18.6 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~132 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) — 18.6 GB, 132.4 tok/s, Runs well

18.6 GB required24.0 GB available

78% VRAM used

Fit status

Runs well

Decode

132.4 tok/s

TTFT

1463 ms

Safe context

52K

Memory

18.6 GB / 24.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGPT-OSS 20B on RTX 4090 24GB
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: 132.4 tok/s decode · 1.5s TTFT (warm) · 331 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	132.4 tok/s	798 ms	52K
Coding	S	Runs well	132.4 tok/s	1463 ms	52K
Agentic Coding	S	Tight fit	132.4 tok/s	2128 ms	52K
Reasoning	S	Runs well	132.4 tok/s	1729 ms	52K
RAG	S	Tight fit	132.4 tok/s	2659 ms	52K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on RTX 4090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	S86
Q3_K_S	3	10.3 GB	Low	S88
NVFP4	4	11.8 GB	Medium	S89
Q4_K_M	4	12.8 GB	Medium	S89
Q5_K_M	5	15.1 GB	High	S88
Q6_KBest for your GPU	6	17.2 GB	High	S88
Q8_0	8	22.5 GB	Very High	F0
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 4090 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	83.4 tok/s
Qwen 3.5 27B	27B	S	34.8 tok/s
Qwen 3.6 27B	27B	S	20.2 tok/s
Qwen 3.6 35B A3B	35B	A	53.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	119.8 tok/s

Frequently asked questions

Can RTX 4090 24GB run GPT-OSS 20B?

Yes, RTX 4090 24GB can run GPT-OSS 20B with a S grade (Runs well). Expected decode speed: 132.4 tok/s.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 18.6 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 RTX 4090 24GB?

On RTX 4090 24GB, GPT-OSS 20B achieves approximately 132.4 tokens per second decode speed with a time-to-first-token of 1463ms using Q4_K_M quantization.

Can RTX 4090 24GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on RTX 4090 24GB receives a S grade with 132.4 tok/s and 52K context.

What context window can GPT-OSS 20B use on RTX 4090 24GB?

On RTX 4090 24GB, GPT-OSS 20B can safely use up to 52K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 4090 24GB 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-rtx-4090-24gb" 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

132.4 tok/s

798 ms

52K

Coding

Runs well

132.4 tok/s

1463 ms

52K

Agentic Coding

Tight fit

132.4 tok/s

2128 ms

52K

Reasoning

Runs well

132.4 tok/s

1729 ms

52K

RAG

Tight fit

132.4 tok/s

2659 ms

52K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on RTX 4090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	S86
Q3_K_S	3	10.3 GB	Low	S88
NVFP4	4	11.8 GB	Medium	S89
Q4_K_M	4	12.8 GB	Medium	S89
Q5_K_M	5	15.1 GB	High	S88
Q6_KBest for your GPU	6	17.2 GB	High	S88
Q8_0	8	22.5 GB	Very High	F0
F16	16	43.1 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

83.4 tok/s

Qwen 3.5 27B

27B

34.8 tok/s

Qwen 3.6 27B

27B

20.2 tok/s

Qwen 3.6 35B A3B

35B

53.4 tok/s

Qwen3-VL 30B A3B Instruct

30B

119.8 tok/s

Frequently asked questions

Can RTX 4090 24GB run GPT-OSS 20B?

Yes, RTX 4090 24GB can run GPT-OSS 20B with a S grade (Runs well). Expected decode speed: 132.4 tok/s.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 18.6 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 RTX 4090 24GB?

On RTX 4090 24GB, GPT-OSS 20B achieves approximately 132.4 tokens per second decode speed with a time-to-first-token of 1463ms using Q4_K_M quantization.

Can RTX 4090 24GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on RTX 4090 24GB receives a S grade with 132.4 tok/s and 52K context.

What context window can GPT-OSS 20B use on RTX 4090 24GB?

On RTX 4090 24GB, GPT-OSS 20B can safely use up to 52K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.