Can GPT-OSS 20B run on RTX 4080 Super 16GB?

Q: What should I upgrade first if GPT-OSS 20B feels slow on RTX 4080 Super 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

A81Great

Estimated — low-sample bucket· few comparable runs

GPT-OSS 20B needs ~17.8 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~66 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 17.8 GB, 63.6 tok/s, Very compromised (needs ~1.3 GB host RAM)

17.8 GB required16.0 GB available

111% VRAM needed

1.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.3 GB host RAM)

Decode

63.6 tok/s

TTFT

3043 ms

Safe context

Memory

17.8 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGPT-OSS 20B on RTX 4080 Super 16GB
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: 63.6 tok/s decode · 3.0s TTFT (warm) · 159 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload	76.4 tok/s	1382 ms	5K
Coding	A	Very compromised	65.7 tok/s	2945 ms	5K
Agentic Coding	F	Too heavy	50.1 tok/s	5618 ms	5K
Reasoning	A	Very compromised	65.7 tok/s	3480 ms	5K
RAG	F	Too heavy	50.1 tok/s	7022 ms	5K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	S90
Q3_K_S	3	10.3 GB	Low	S89
NVFP4Best for your GPU

Get started

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

Run

ollama run gpt-oss

Frequently asked questions

Can RTX 4080 Super 16GB run GPT-OSS 20B?

Yes, RTX 4080 Super 16GB can run GPT-OSS 20B with a A grade (Very compromised). Expected decode speed: 65.7 tok/s.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 17.8 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 4080 Super 16GB?

On RTX 4080 Super 16GB, GPT-OSS 20B achieves approximately 65.7 tokens per second decode speed with a time-to-first-token of 2945ms using Q4_K_M quantization.

Can RTX 4080 Super 16GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on RTX 4080 Super 16GB receives a A grade with 65.7 tok/s and 5K context.

What context window can GPT-OSS 20B use on RTX 4080 Super 16GB?

On RTX 4080 Super 16GB, GPT-OSS 20B can safely use up to 5K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if GPT-OSS 20B feels slow on RTX 4080 Super 16GB?

See all results for RTX 4080 Super 16GB 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-4080-super-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: