Can Granite 4.1 3B run on GTX 1650 4GB?

BARELY — Tight on Memory

B56Good

○Estimated from fit model

Granite 4.1 3B needs ~4.4 GB VRAM. GTX 1650 4GB has 4.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 4.4 GB, 22.9 tok/s, Very compromised (needs ~0.1 GB host RAM)

4.4 GB required4.0 GB available

110% VRAM needed

0.4 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.1 GB host RAM)

Decode

22.9 tok/s

TTFT

8451 ms

Safe context

11K

Memory

4.4 GB / 4.0 GB

Offload

10%

Memory breakdown

Weights1.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

See how fast it feelsGranite 4.1 3B 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: 22.9 tok/s decode · 8.5s TTFT (warm) · 57 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 {ram} 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	B	Tight fit	37.8 tok/s	2796 ms	11K
Coding	B	Very compromised	21.2 tok/s	9128 ms	11K
Agentic Coding	F	Too heavy	13.3 tok/s	21160 ms	11K
Reasoning	B	Very compromised (needs ~0.1 GB host RAM)	22.9 tok/s	9988 ms	11K
RAG	F	Too heavy	13.3 tok/s	26450 ms	11K

Quantization options

How Granite 4.1 3B (3B params) fits at each quantization level on GTX 1650 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	A72
Q3_K_S	3	1.5 GB	Low	A72
NVFP4	4

Get started

Copy-paste commands to run Granite 4.1 3B on your machine.

Run

ollama run granite4.1:3b

Upgrade options

Hardware that runs Granite 4.1 3B well

👁 NVIDIA
GTX 1660 Super 6GBBudget pick

6 GB VRAM (+2)336 GB/s (+208)

Removes host-memory offload, which is usually the single biggest latency and throughput win.42 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 83%.

~$229 MSRP

👁 NVIDIA
GTX 1060 6GBBest value

6 GB VRAM (+2)192 GB/s (+64)

Removes host-memory offload, which is usually the single biggest latency and throughput win.42 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 83%.

~$249 MSRP

👁 NVIDIA
RTX 3050 8GBNVIDIA upgrade

8 GB VRAM (+4)224 GB/s (+96)

Removes host-memory offload, which is usually the single biggest latency and throughput win.36 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 57%.

~$249 MSRP

Frequently asked questions

See all results for GTX 1650 4GB See all hardware for Granite 4.1 3B

URL: https://willitrunai.com/can-run/granite-4.1-3b-on-gtx-1650-4gb

⇱

Can Granite 4.1 3B run on GTX 1650 4GB?

BARELY — Tight on Memory

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Granite 4.1 3B well

Frequently asked questions