Can Nemotron Nano 8B run on RTX 2060 6GB?

Q: What should I upgrade first if Nemotron Nano 8B feels slow on RTX 2060 6GB?

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

A75Great

Estimated from fit model

Nemotron Nano 8B needs ~6.9 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q2_K quantization, expect ~30 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.

Nemotron Nano 8B at Q4_K_M needs 8.6 GB — too much for RTX 2060 6GB (6.0 GB). Runs at Q2_K (6.9 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.6 GB, exceeds 6.0 GB available

8.6 GB required6.0 GB available

143% VRAM needed

2.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.8 tok/s

TTFT

13987 ms

Safe context

Memory

8.6 GB / 6.0 GB

Offload

30%

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNemotron Nano 8B on RTX 2060 6GB
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: 13.8 tok/s decode · 14.0s TTFT (warm) · 35 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.

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

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 0.4 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	18.0 tok/s	5861 ms	4K
Coding	F	Too heavy	12.9 tok/s	15036 ms	4K
Agentic Coding	F	Too heavy	8.8 tok/s	31840 ms	4K
Reasoning	F	Too heavy	13.8 tok/s	16530 ms	4K
RAG	F	Too heavy	8.8 tok/s	39800 ms	4K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.1 GB	Low	S89
Q3_K_S	3	3.9 GB	Low	F0

Get started

Copy-paste commands to run Nemotron Nano 8B on your machine.

Run

lms load Llama-3.1-Nemotron-Nano-8B-v1 && lms server start

Upgrade options

Hardware that runs Nemotron Nano 8B well

RTX 3050 8GBBest value

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.19.8 tok/s decode

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

Raises estimated decode speed by about 43%.

~$249 MSRP

RTX 5060 8GBNVIDIA upgrade

8 GB VRAM (+2)448 GB/s (+112)

Makes the model fit on the accelerator instead of staying completely out of reach.37.6 tok/s decode

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

Raises estimated decode speed by about 172%.

~$299 MSRP

RTX 3060 12GBBudget pick

12 GB VRAM (+6)360 GB/s (+24)

Makes the model fit on the accelerator instead of staying completely out of reach.52.3 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.

~$329 MSRP

Frequently asked questions

Can RTX 2060 6GB run Nemotron Nano 8B?

Yes, RTX 2060 6GB can run Nemotron Nano 8B at Q2_K quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 8.6 GB which exceeds available memory, but at Q2_K it needs only 6.9 GB. Expected decode speed: 30.4 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 8.6 GB at Q4_K_M quantization. On RTX 2060 6GB, it fits at Q2_K using 6.9 GB.

What is the best quantization for Nemotron Nano 8B?

The recommended quantization is Q4_K_M, but on RTX 2060 6GB the best fitting quantization is Q2_K, which uses 6.9 GB.

What speed will Nemotron Nano 8B run at on RTX 2060 6GB?

On RTX 2060 6GB, Nemotron Nano 8B achieves approximately 30.4 tokens per second decode speed with a time-to-first-token of 6377ms using Q2_K quantization.

Can RTX 2060 6GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on RTX 2060 6GB receives a F grade with 12.9 tok/s and 4K context.

What context window can Nemotron Nano 8B use on RTX 2060 6GB?

On RTX 2060 6GB, Nemotron Nano 8B can safely use up to 9K 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 2060 6GB See all hardware for Nemotron Nano 8B

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