Can Nemotron Nano 8B run on RTX 2080 Ti 11GB?

YES — Runs Great

S92Excellent

○Estimated from fit model

Nemotron Nano 8B needs ~8.8 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~88 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

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, 88.2 tok/s, Runs well

8.8 GB required11.0 GB available

80% VRAM used

Fit status

Runs well

Decode

88.2 tok/s

TTFT

2195 ms

Safe context

34K

Memory

8.8 GB / 11.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on RTX 2080 Ti 11GB
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: 88.2 tok/s decode · 2.2s TTFT (warm) · 221 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	88.2 tok/s	1197 ms	34K
Coding	S	Runs well	88.2 tok/s	2195 ms	34K
Agentic Coding	S	Runs with offload	88.2 tok/s	3193 ms	34K
Reasoning	S	Runs well	88.2 tok/s	2594 ms	34K
RAG	S	Runs with offload	88.2 tok/s	3991 ms	34K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	S85
Q3_K_S	3	3.9 GB	Low	S87
NVFP4	4	4.5 GB	Medium	S87
Q4_K_M	4	4.9 GB	Medium	S88
Q5_K_M	5	5.8 GB	High	S87
Q6_KBest for your GPU	6	6.6 GB	High	S87
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	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

Your hardware

More models your RTX 2080 Ti 11GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 9B	9B	S	78.4 tok/s
👁 Alibaba Qwen 3 14B	14B	A	25.7 tok/s

Frequently asked questions

See all results for RTX 2080 Ti 11GB See all hardware for Nemotron Nano 8B

URL: https://willitrunai.com/can-run/nemotron-nano-8b-on-rtx-2080-ti-11gb

⇱ Nemotron Nano 8B on RTX 2080 Ti 11GB? YES