Can GPT-OSS 120B run on RTX 6000 Ada 48GB?

Q: What should I upgrade first if GPT-OSS 120B feels slow on RTX 6000 Ada 48GB?

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.

YES — With Q2_K

A77Great

Estimated from fit model

GPT-OSS 120B needs ~56.2 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q2_K quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: 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.

GPT-OSS 120B at Q4_K_M needs 82.0 GB — too much for RTX 6000 Ada 48GB (48.0 GB). Runs at Q2_K (56.2 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 82.0 GB, exceeds 48.0 GB available

82.0 GB required48.0 GB available

171% VRAM needed

34.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.9 tok/s

TTFT

66357 ms

Safe context

Memory

82.0 GB / 48.0 GB

Offload

40%

Memory breakdown

Weights71.4 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGPT-OSS 120B on RTX 6000 Ada 48GB
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: 2.9 tok/s decode · 66.4s TTFT (warm) · 7 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 6.7 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	3.1 tok/s	33962 ms	4K
Coding	F	Too heavy	2.9 tok/s	66357 ms	4K
Agentic Coding	F	Too heavy	2.6 tok/s	109023 ms	4K
Reasoning	F	Too heavy	2.9 tok/s	78422 ms	4K
RAG	F	Too heavy	2.6 tok/s	136279 ms	4K

Quantization options

How GPT-OSS 120B (117B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 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

Get started

Copy-paste commands to run GPT-OSS 120B on your machine.

Run

ollama run gpt-oss:120b

Upgrade options

Hardware that runs GPT-OSS 120B well

RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

96 GB VRAM (+48)1792 GB/s (+832)

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 (+48)1597 GB/s (+637)

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 (+48)4000 GB/s (+3040)

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 6000 Ada 48GB run GPT-OSS 120B?

Yes, RTX 6000 Ada 48GB can run GPT-OSS 120B at Q2_K quantization (Very compromised (needs ~6.7 GB host RAM)). The recommended Q4_K_M requires 82.0 GB which exceeds available memory, but at Q2_K it needs only 56.2 GB. Expected decode speed: 8.6 tok/s.

How much VRAM does GPT-OSS 120B need?

GPT-OSS 120B (117B parameters) requires approximately 82.0 GB at Q4_K_M quantization. On RTX 6000 Ada 48GB, it fits at Q2_K using 56.2 GB.

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

The recommended quantization is Q4_K_M, but on RTX 6000 Ada 48GB the best fitting quantization is Q2_K, which uses 56.2 GB.

What speed will GPT-OSS 120B run at on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, GPT-OSS 120B achieves approximately 8.6 tokens per second decode speed with a time-to-first-token of 22571ms using Q2_K quantization.

Can RTX 6000 Ada 48GB run GPT-OSS 120B for coding?

For coding workloads, GPT-OSS 120B on RTX 6000 Ada 48GB receives a F grade with 2.9 tok/s and 4K context.

What context window can GPT-OSS 120B use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, GPT-OSS 120B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 6000 Ada 48GB See all hardware for GPT-OSS 120B

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