Can GPT-OSS 20B run on RTX 3500 Ada Laptop 12GB?

YES — With Q2_K

A80Great

Estimated from fit model

GPT-OSS 20B needs ~13.0 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q2_K quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: 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.

GPT-OSS 20B at Q4_K_M needs 17.7 GB — too much for RTX 3500 Ada Laptop 12GB (12.0 GB). Runs at Q2_K (13.0 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 17.7 GB, exceeds 12.0 GB available

17.7 GB required12.0 GB available

148% VRAM needed

5.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

15.7 tok/s

TTFT

12352 ms

Safe context

Memory

17.7 GB / 12.0 GB

Offload

30%

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGPT-OSS 20B on RTX 3500 Ada Laptop 12GB
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: 15.7 tok/s decode · 12.4s TTFT (warm) · 39 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 0.6 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	F	Too heavy	18.2 tok/s	5794 ms	4K
Coding	F	Too heavy	15.7 tok/s	12352 ms	4K
Agentic Coding	F	Too heavy	11.9 tok/s	23599 ms	4K
Reasoning	F	Too heavy	15.7 tok/s	14598 ms	4K
RAG	F	Too heavy	11.9 tok/s	29499 ms	4K

Inference speed

GPT-OSS 20B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for GPT-OSS 20B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~231 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	230.5	Fits
RTX 4090 24GB	24 GB	Q4_K_M	147.1	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	132.7	Fits
RTX 3090 24GB	24 GB	Q4_K_M	125.8	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	106.9	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	89.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	84.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	68.2	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	66.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	66.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	46.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	42.2	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	40.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	24.2	Too big
RTX 3060 12GB	12 GB	Q4_K_M	15.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	5.7	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	8.2 GB	Low	S90
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

Get started

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

Run

ollama run gpt-oss

Opciones de mejora

Hardware que ejecuta bien GPT-OSS 20B

RTX 5060 Ti 16GBMejor relación calidad-precio

16 GB VRAM (+4)448 GB/s (+112)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.31.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 103%.

~$449 MSRP

RTX 4060 Ti 16GBMejora NVIDIA

16 GB VRAM (+4)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.23.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 50%.

~$499 MSRP

RTX 4000 Ada 20GBOpción económica

20 GB VRAM (+8)360 GB/s (+24)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.53.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,250 MSRP

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run GPT-OSS 20B?

Yes, RTX 3500 Ada Laptop 12GB can run GPT-OSS 20B at Q2_K quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 17.7 GB which exceeds available memory, but at Q2_K it needs only 13.0 GB. Expected decode speed: 39.5 tok/s.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 17.7 GB at Q4_K_M quantization. On RTX 3500 Ada Laptop 12GB, it fits at Q2_K using 13.0 GB.

What is the best quantization for GPT-OSS 20B?

The recommended quantization is Q4_K_M, but on RTX 3500 Ada Laptop 12GB the best fitting quantization is Q2_K, which uses 13.0 GB.

What speed will GPT-OSS 20B run at on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, GPT-OSS 20B achieves approximately 39.5 tokens per second decode speed with a time-to-first-token of 4905ms using Q2_K quantization.

Can RTX 3500 Ada Laptop 12GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on RTX 3500 Ada Laptop 12GB receives a F grade with 15.7 tok/s and 4K context.

What context window can GPT-OSS 20B use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, GPT-OSS 20B can safely use up to 9K tokens of context at Q2_K quantization. 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 3500 Ada Laptop 12GB?

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.

See all results for RTX 3500 Ada Laptop 12GB See all hardware for GPT-OSS 20B

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