Can GPT-OSS 20B run on RTX PRO 4000 Blackwell 24GB?

YES — Runs Great

S95Excellent

○Estimated from fit model

GPT-OSS 20B needs ~18.9 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~108 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) — 18.9 GB, 108.4 tok/s, Runs well

18.9 GB required24.0 GB available

79% VRAM used

Fit status

Runs well

Decode

108.4 tok/s

TTFT

1787 ms

Safe context

50K

Memory

18.9 GB / 24.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGPT-OSS 20B on RTX PRO 4000 Blackwell 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: 108.4 tok/s decode · 1.8s TTFT (warm) · 271 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	108.4 tok/s	974 ms	50K
Coding	S	Runs well	108.4 tok/s	1787 ms	50K
Agentic Coding	S	Tight fit	108.4 tok/s	2599 ms	50K
Reasoning	S	Runs well	108.4 tok/s	2111 ms	50K
RAG	S	Tight fit	108.4 tok/s	3248 ms	50K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on RTX PRO 4000 Blackwell 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 PRO 4000 Blackwell 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	85.4 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	37 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	37.1 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	88.3 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	A	49.1 tok/s

Frequently asked questions

See all results for RTX PRO 4000 Blackwell 24GB See all hardware for GPT-OSS 20B

URL: https://willitrunai.com/can-run/gpt-oss-20b-on-rtx-pro-4000-blackwell-24gb

⇱ GPT-OSS 20B on RTX PRO 4000 Blackwell 24GB? YES