Can GPT-OSS 20B run on RTX 2060 Super 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

GPT-OSS 20B needs ~17.3 GB but RTX 2060 Super 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: 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) — 17.3 GB, exceeds 8.0 GB available

17.3 GB required8.0 GB available

216% VRAM needed

9.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.5 tok/s

TTFT

25875 ms

Safe context

Memory

17.3 GB / 8.0 GB

Offload

50%

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGPT-OSS 20B on RTX 2060 Super 8GB
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: 7.5 tok/s decode · 25.9s TTFT (warm) · 19 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 17.3 GB, but this setup only exposes 8.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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	7.9 tok/s	13355 ms	4K
Coding	F	Too heavy	7.0 tok/s	27815 ms	4K
Agentic Coding	F	Too heavy	7.5 tok/s	37636 ms	4K
Reasoning	F	Too heavy	7.5 tok/s	30579 ms	4K
RAG	F	Too heavy	7.5 tok/s	47045 ms	4K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 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

Upgrade options

Hardware that runs GPT-OSS 20B well

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)

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

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

Raises estimated decode speed by about 325%.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)

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

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

Raises estimated decode speed by about 213%.

~$499 MSRP

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+12)

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

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 2060 Super 8GB run GPT-OSS 20B?

No, GPT-OSS 20B requires more memory than RTX 2060 Super 8GB provides.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 17.3 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 2060 Super 8GB?

On RTX 2060 Super 8GB, GPT-OSS 20B achieves approximately 7.0 tokens per second decode speed with a time-to-first-token of 27815ms using Q4_K_M quantization.

Can RTX 2060 Super 8GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on RTX 2060 Super 8GB receives a F grade with 7.0 tok/s and 4K context.

What context window can GPT-OSS 20B use on RTX 2060 Super 8GB?

On RTX 2060 Super 8GB, 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 2060 Super 8GB?

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 2060 Super 8GB See all hardware for GPT-OSS 20B

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