Can Nemotron Nano 9B v2 run on GTX 1650 4GB?

Q: What should I upgrade first if Nemotron Nano 9B v2 feels slow on GTX 1650 4GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

Nemotron Nano 9B v2 needs ~9.5 GB but GTX 1650 4GB only has 4.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: BasicBottleneck: Memory capacity

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) — 9.5 GB, exceeds 4.0 GB available

9.5 GB required4.0 GB available

238% VRAM needed

5.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

9.5 GB / 4.0 GB

Offload

60%

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNemotron Nano 9B v2 on GTX 1650 4GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 9.5 GB, but this setup only exposes 4.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on GTX 1650 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	F0
Q3_K_S	3	4.4 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Nemotron Nano 9B v2 well

RTX 3060 12GBBudget pick

12 GB VRAM (+8)360 GB/s (+232)

Makes the model fit on the accelerator instead of staying completely out of reach.46.5 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

RTX 5060 Ti 16GBBest value

16 GB VRAM (+12)448 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.54.4 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.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+12)288 GB/s (+160)

Makes the model fit on the accelerator instead of staying completely out of reach.41.2 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.

~$499 MSRP

Frequently asked questions

Can GTX 1650 4GB run Nemotron Nano 9B v2?

No, Nemotron Nano 9B v2 requires more memory than GTX 1650 4GB provides.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 9B v2?

The recommended quantization for Nemotron Nano 9B v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Nano 9B v2 run at on GTX 1650 4GB?

On GTX 1650 4GB, Nemotron Nano 9B v2 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can GTX 1650 4GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on GTX 1650 4GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Nemotron Nano 9B v2 use on GTX 1650 4GB?

On GTX 1650 4GB, Nemotron Nano 9B v2 can safely use up to 4K 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 9B v2 feels slow on GTX 1650 4GB?

See all results for GTX 1650 4GB See all hardware for Nemotron Nano 9B v2

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