Can Gemma 3 12B run on GTX 1660 Super 6GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Gemma 3 12B needs ~14.0 GB but GTX 1660 Super 6GB only has 6.0 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) — 14.0 GB, exceeds 6.0 GB available

14.0 GB required6.0 GB available

233% VRAM needed

8.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.0 tok/s

TTFT

48689 ms

Safe context

Memory

14.0 GB / 6.0 GB

Offload

60%

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 3 12B on GTX 1660 Super 6GB
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: 4.0 tok/s decode · 48.7s TTFT (warm) · 10 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 14.0 GB, but this setup only exposes 6.0 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	4.6 tok/s	23060 ms	4K
Coding	F	Too heavy	4.0 tok/s	48689 ms	4K
Agentic Coding	F	Too heavy	4.0 tok/s	70821 ms	4K
Reasoning	F	Too heavy	4.0 tok/s	57542 ms	4K
RAG	F	Too heavy	4.0 tok/s	88526 ms	4K

Inference speed

Gemma 3 12B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 3 12B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~168 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	168.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	109.9	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	99.1	Fits
RTX 3090 24GB	24 GB	Q4_K_M	94.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	87.6	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	60.5	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	50.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	47.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	43.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	34.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	32.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	31.6	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	26.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	21.5	Heavy offload
RTX 3060 12GB	12 GB	Q4_K_M	13.1	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	6.4	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on GTX 1660 Super 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	F0
Q3_K_S	3	5.9 GB	Low	F0
NVFP4	4	6.7 GB	Medium	F0
Q4_K_M	4	7.3 GB	Medium	F0
Q5_K_M	5	8.6 GB	High	F0
Q6_K	6	9.8 GB	High	F0
Q8_0	8	12.8 GB	Very High	F0
F16	16	24.6 GB	Maximum	F0

アップグレードオプション

Gemma 3 12Bを快適に動かすハードウェア

RTX 5060 Ti 16GBコスパ重視

16 GB VRAM (+10)448 GB/s (+112)

Makes the model fit on the accelerator instead of staying completely out of reach.39.8 tok/sデコード

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 16GBコスパ最良

16 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.30.2 tok/sデコード

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 2000 Ada 16GBNVIDIAアップグレード

16 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.31.4 tok/sデコード

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.

〜$625 MSRP

Frequently asked questions

Can GTX 1660 Super 6GB run Gemma 3 12B?

No, Gemma 3 12B requires more memory than GTX 1660 Super 6GB provides.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 14.0 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 GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, Gemma 3 12B achieves approximately 4.0 tokens per second decode speed with a time-to-first-token of 48689ms using Q4_K_M quantization.

Can GTX 1660 Super 6GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on GTX 1660 Super 6GB receives a F grade with 4.0 tok/s and 4K context.

What context window can Gemma 3 12B use on GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, 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 GTX 1660 Super 6GB?

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 GTX 1660 Super 6GB See all hardware for Gemma 3 12B

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