Can Nemotron Nano 8B run on RTX 4070 Super 12GB?

YES — Runs Great

S92Excellent

○Estimated from fit model

Nemotron Nano 8B needs ~9.2 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~86 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 9.2 GB, 85.5 tok/s, Runs well

9.2 GB required12.0 GB available

77% VRAM used

Fit status

Runs well

Decode

85.5 tok/s

TTFT

2265 ms

Safe context

39K

Memory

9.2 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on RTX 4070 Super 12GB
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: 85.5 tok/s decode · 2.3s TTFT (warm) · 214 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	85.5 tok/s	1235 ms	39K
Coding	S	Runs well	85.5 tok/s	2265 ms	39K
Agentic Coding	S	Tight fit	85.5 tok/s	3294 ms	39K
Reasoning	S	Runs well	85.5 tok/s	2676 ms	39K
RAG	S	Tight fit	85.5 tok/s	4117 ms	39K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A85
Q3_K_S	3	3.9 GB	Low	S86
NVFP4	4	4.5 GB	Medium	S86
Q4_K_M	4	4.9 GB	Medium	S87
Q5_K_M	5	5.8 GB	High	S87
Q6_K	6	6.6 GB	High	S87
Q8_0Best for your GPU	8	8.6 GB	Very High	S87
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 4070 Super 12GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 9B	9B	S	76 tok/s
👁 Alibaba Qwen 3 14B	14B	A	29.3 tok/s

Frequently asked questions

See all results for RTX 4070 Super 12GB See all hardware for Nemotron Nano 8B

URL: https://willitrunai.com/can-run/nemotron-nano-8b-on-rtx-4070-super-12gb

⇱ Nemotron Nano 8B on RTX 4070 Super 12GB? YES