Can GPT-OSS 20B run on RTX 3080 10GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

GPT-OSS 20B needs ~17.5 GB but RTX 3080 10GB only has 10.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity

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) — 17.5 GB, exceeds 10.0 GB available

17.5 GB required10.0 GB available

175% VRAM needed

7.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

25.8 tok/s

TTFT

7517 ms

Safe context

Memory

17.5 GB / 10.0 GB

Offload

40%

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGPT-OSS 20B on RTX 3080 10GB
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: 25.8 tok/s decode · 7.5s TTFT (warm) · 64 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 17.5 GB, but this setup only exposes 10.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	30.0 tok/s	3520 ms	4K
Coding	F	Too heavy	24.0 tok/s	8081 ms	4K
Agentic Coding	F	Too heavy	19.6 tok/s	14403 ms	4K
Reasoning	F	Too heavy	25.8 tok/s	8884 ms	4K
RAG	F	Too heavy	19.6 tok/s	18004 ms	4K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	F0
Q3_K_S	3	10.3 GB	Low	F0
NVFP4	4	11.8 GB	Medium	F0
Q4_K_M	4	12.8 GB	Medium	F0
Q5_K_M	5	15.1 GB	High	F0
Q6_K	6	17.2 GB	High	F0
Q8_0	8	22.5 GB	Very High	F0
F16	16	43.1 GB	Maximum	F0

升级选项

能流畅运行 GPT-OSS 20B 的硬件

RTX 5060 Ti 16GB最佳性价比

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.31.9 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA 升级

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.23.5 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Adds memory headroom for longer context windows and future model growth.

~$499 MSRP

RTX 4000 Ada 20GB经济之选

20 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.53.9 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,250 MSRP

Frequently asked questions

Can RTX 3080 10GB run GPT-OSS 20B?

No, GPT-OSS 20B requires more memory than RTX 3080 10GB provides.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 17.5 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 3080 10GB?

On RTX 3080 10GB, GPT-OSS 20B achieves approximately 24.0 tokens per second decode speed with a time-to-first-token of 8081ms using Q4_K_M quantization.

Can RTX 3080 10GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on RTX 3080 10GB receives a F grade with 24.0 tok/s and 4K context.

What context window can GPT-OSS 20B use on RTX 3080 10GB?

On RTX 3080 10GB, GPT-OSS 20B can safely use up to 4K 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 3080 10GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 3080 10GB See all hardware for GPT-OSS 20B

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