Can GPT-OSS 120B run on RTX 3000 Ada Laptop 8GB?

Q: What should I upgrade first if GPT-OSS 120B feels slow on RTX 3000 Ada Laptop 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.

NO — Won't Fit

F0Won't run

Estimated from fit model

GPT-OSS 120B needs ~78.0 GB but RTX 3000 Ada Laptop 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: 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) — 78.0 GB, exceeds 8.0 GB available

78.0 GB required8.0 GB available

975% VRAM needed

70.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

78.0 GB / 8.0 GB

Offload

90%

Memory breakdown

Weights71.4 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGPT-OSS 120B on RTX 3000 Ada Laptop 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 78.0 GB, but this setup only exposes 8.0 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	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How GPT-OSS 120B (117B params) fits at each quantization level on RTX 3000 Ada Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	45.6 GB	Low	F0
Q3_K_S	3	57.3 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs GPT-OSS 120B well

RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

96 GB VRAM (+88)1792 GB/s (+1504)

Makes the model fit on the accelerator instead of staying completely out of reach.22.9 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.

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBBest value

96 GB VRAM (+88)1597 GB/s (+1309)

Makes the model fit on the accelerator instead of staying completely out of reach.20.4 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.

~$9,999 MSRP

NVIDIA H20 96GBNVIDIA upgrade

96 GB VRAM (+88)4000 GB/s (+3712)

Makes the model fit on the accelerator instead of staying completely out of reach.49.4 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.

~$12,000 MSRP

Frequently asked questions

Can RTX 3000 Ada Laptop 8GB run GPT-OSS 120B?

No, GPT-OSS 120B requires more memory than RTX 3000 Ada Laptop 8GB provides.

How much VRAM does GPT-OSS 120B need?

GPT-OSS 120B (117B parameters) requires approximately 78.0 GB of memory with Q4_K_M quantization.

What is the best quantization for GPT-OSS 120B?

The recommended quantization for GPT-OSS 120B is Q4_K_M, which balances quality and memory efficiency.

What speed will GPT-OSS 120B run at on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, GPT-OSS 120B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RTX 3000 Ada Laptop 8GB run GPT-OSS 120B for coding?

For coding workloads, GPT-OSS 120B on RTX 3000 Ada Laptop 8GB receives a F grade with 2.0 tok/s and 4K context.

What context window can GPT-OSS 120B use on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, GPT-OSS 120B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if GPT-OSS 120B feels slow on RTX 3000 Ada Laptop 8GB?

See all results for RTX 3000 Ada Laptop 8GB See all hardware for GPT-OSS 120B

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