Will It Run AI

Can Nemotron Nano 8B run on RTX 2000 Ada Laptop 8GB?

BARELY — Tight on Memory

A75Great
Estimated from fit model

Nemotron Nano 8B needs ~8.8 GB VRAM. RTX 2000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload
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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) — 8.8 GB, 25.1 tok/s, Very compromised (needs ~0.5 GB host RAM)
8.8 GB required8.0 GB available
110% VRAM needed

0.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.5 GB host RAM)

Decode

25.1 tok/s

TTFT

7724 ms

Safe context

9K

Memory

8.8 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on RTX 2000 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: 25.1 tok/s decode · 7.7s TTFT (warm) · 63 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 0.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatSRuns with offload41.2 tok/s2565 ms9K
CodingAVery compromised (needs ~0.5 GB host RAM)25.1 tok/s7724 ms9K
Agentic CodingFToo heavy15.3 tok/s18391 ms9K
ReasoningAVery compromised (needs ~0.5 GB host RAM)25.1 tok/s9129 ms9K
RAGFToo heavy16.5 tok/s21385 ms9K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on RTX 2000 Ada Laptop 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowS89
Q3_K_S
3
3.9 GB
LowS88
NVFP4
4
4.5 GB
MediumS88
Q4_K_MBest for your GPU
4
4.9 GB
MediumS88
Q5_K_M
5
5.8 GB
HighF0
Q6_K
6
6.6 GB
HighF0
Q8_0
8
8.6 GB
Very HighF0
F16
16
16.4 GB
MaximumF0

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

Frequently asked questions

Can RTX 2000 Ada Laptop 8GB run Nemotron Nano 8B?

Yes, RTX 2000 Ada Laptop 8GB can run Nemotron Nano 8B with a A grade (Very compromised (needs ~0.5 GB host RAM)). Expected decode speed: 25.1 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 8B?

The recommended quantization for Nemotron Nano 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Nano 8B run at on RTX 2000 Ada Laptop 8GB?

On RTX 2000 Ada Laptop 8GB, Nemotron Nano 8B achieves approximately 25.1 tokens per second decode speed with a time-to-first-token of 7724ms using Q4_K_M quantization.

Can RTX 2000 Ada Laptop 8GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on RTX 2000 Ada Laptop 8GB receives a A grade with 25.1 tok/s and 9K context.

What context window can Nemotron Nano 8B use on RTX 2000 Ada Laptop 8GB?

On RTX 2000 Ada Laptop 8GB, Nemotron Nano 8B can safely use up to 9K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Nemotron Nano 8B feels slow on RTX 2000 Ada Laptop 8GB?

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 2000 Ada Laptop 8GBSee all hardware for Nemotron Nano 8B
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