Can Gemma 3 12B run on RTX 4070 Laptop 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Gemma 3 12B needs ~14.2 GB but RTX 4070 Laptop 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: 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) — 14.2 GB, exceeds 8.0 GB available

14.2 GB required8.0 GB available

178% VRAM needed

6.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.3 tok/s

TTFT

30964 ms

Safe context

Memory

14.2 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 3 12B on RTX 4070 Laptop 8GB
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: 6.3 tok/s decode · 31.0s TTFT (warm) · 16 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 14.2 GB, but this setup only exposes 8.0 GB of usable VRAM.

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	9.3 tok/s	11355 ms	4K
Coding	F	Too heavy	6.3 tok/s	30964 ms	4K
Agentic Coding	F	Too heavy	4.2 tok/s	67268 ms	4K
Reasoning	F	Too heavy	6.3 tok/s	36594 ms	4K
RAG	F	Too heavy	4.2 tok/s	84085 ms	4K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on RTX 4070 Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	4.7 GB	Low	A83
Q3_K_S	3	5.9 GB	Low	F0

Upgrade options

Hardware that runs Gemma 3 12B well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+8)448 GB/s (+192)

Makes the model fit on the accelerator instead of staying completely out of reach.39.8 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 16GBBest value

16 GB VRAM (+8)288 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.30.2 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

RTX 5070 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)896 GB/s (+640)

Makes the model fit on the accelerator instead of staying completely out of reach.82.3 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.

~$749 MSRP

Frequently asked questions

Can RTX 4070 Laptop 8GB run Gemma 3 12B?

No, Gemma 3 12B requires more memory than RTX 4070 Laptop 8GB provides.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 14.2 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 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, Gemma 3 12B achieves approximately 6.3 tokens per second decode speed with a time-to-first-token of 30964ms using Q4_K_M quantization.

Can RTX 4070 Laptop 8GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on RTX 4070 Laptop 8GB receives a F grade with 6.3 tok/s and 4K context.

What context window can Gemma 3 12B use on RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, Gemma 3 12B 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 Gemma 3 12B feels slow on RTX 4070 Laptop 8GB?

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.

See all results for RTX 4070 Laptop 8GB See all hardware for Gemma 3 12B

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