Can Gemma 3 12B run on RTX 3500 Ada Laptop 12GB?

Q: What should I upgrade first if Gemma 3 12B feels slow on RTX 3500 Ada Laptop 12GB?

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.

BARELY — Tight on Memory

B62Good

Estimated from fit model

Gemma 3 12B needs ~14.3 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 14.3 GB, 13.9 tok/s, Very compromised (needs ~1.2 GB host RAM)

14.3 GB required12.0 GB available

119% VRAM needed

2.3 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.2 GB host RAM)

Decode

13.9 tok/s

TTFT

13898 ms

Safe context

Memory

14.3 GB / 12.0 GB

Offload

20%

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsGemma 3 12B on RTX 3500 Ada Laptop 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: 13.9 tok/s decode · 13.9s TTFT (warm) · 35 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 20% 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 1.2 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	A	Runs with offload	26.9 tok/s	3929 ms	8K
Coding	B	Very compromised (needs ~1.2 GB host RAM)	13.9 tok/s	13898 ms	8K
Agentic Coding	F	Too heavy	7.5 tok/s	37512 ms	8K
Reasoning	B	Very compromised (needs ~1.2 GB host RAM)	13.9 tok/s	16424 ms	8K
RAG	F	Too heavy	7.5 tok/s	46890 ms

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A81
Q3_K_S	3	5.9 GB	Low	A82
NVFP4	4

Get started

Copy-paste commands to run Gemma 3 12B on your machine.

Run

ollama run gemma3:12b

Upgrade options

Hardware that runs Gemma 3 12B well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+4)448 GB/s (+112)

Removes host-memory offload, which is usually the single biggest latency and throughput win.29.3 tok/s decode

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

Raises estimated decode speed by about 111%.

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.23 tok/s decode

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

Raises estimated decode speed by about 65%.

~$499 MSRP

RTX 5070 Ti 16GBNVIDIA upgrade

16 GB VRAM (+4)896 GB/s (+560)

Removes host-memory offload, which is usually the single biggest latency and throughput win.55.2 tok/s decode

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

Raises estimated decode speed by about 297%.

~$749 MSRP

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run Gemma 3 12B?

Yes, RTX 3500 Ada Laptop 12GB can run Gemma 3 12B with a B grade (Very compromised (needs ~1.2 GB host RAM)). Expected decode speed: 13.9 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 14.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 12B?

The recommended quantization for Gemma 3 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 12B run at on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Gemma 3 12B achieves approximately 13.9 tokens per second decode speed with a time-to-first-token of 13898ms using Q4_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on RTX 3500 Ada Laptop 12GB receives a B grade with 13.9 tok/s and 8K context.

What context window can Gemma 3 12B use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Gemma 3 12B can safely use up to 8K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 3 12B feels slow on RTX 3500 Ada Laptop 12GB?

See all results for RTX 3500 Ada Laptop 12GB See all hardware for Gemma 3 12B

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