Can Gemma 4 E2B run on GTX 1660 Super 6GB?

YES — Tight Fit

A75Great

Estimated from fit model

Gemma 4 E2B needs ~5.4 GB VRAM. GTX 1660 Super 6GB has 6.0 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced

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) — 5.4 GB, 64.6 tok/s, Tight fit

5.4 GB required6.0 GB available

90% VRAM used

Fit status

Tight fit

Decode

64.6 tok/s

TTFT

2997 ms

Safe context

33K

Memory

5.4 GB / 6.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsGemma 4 E2B 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: 64.6 tok/s decode · 3.0s TTFT (warm) · 162 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	64.6 tok/s	1635 ms	33K
Coding	A	Tight fit	59.4 tok/s	3259 ms	33K
Agentic Coding	A	Runs with offload	64.6 tok/s	4359 ms	33K
Reasoning	A	Tight fit	64.6 tok/s	3542 ms	33K
RAG	A	Runs with offload	64.6 tok/s	5449 ms	33K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on GTX 1660 Super 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	A77
Q3_K_S	3	2.5 GB	Low	A77
NVFP4	4	2.9 GB	Medium	A77
Q4_K_MBest for your GPU	4	3.1 GB	Medium	A77
Q5_K_M	5	3.7 GB	High	F0
Q6_K	6	4.2 GB	High	F0
Q8_0	8	5.5 GB	Very High	F0
F16	16	10.5 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 4 E2B on your machine.

Run

ollama run gemma4:e2b

Your hardware

More models your GTX 1660 Super 6GB can run

Model	Params	Grade	Decode
Qwen 2.5 VL 7B	7B	B	25 tok/s
Qwen 2.5 7B	7B	B	25 tok/s
Codestral Mamba 7B	7B	B	26 tok/s

Frequently asked questions

Can GTX 1660 Super 6GB run Gemma 4 E2B?

Yes, GTX 1660 Super 6GB can run Gemma 4 E2B with a A grade (Tight fit). Expected decode speed: 59.4 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 5.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 E2B?

The recommended quantization for Gemma 4 E2B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 E2B run at on GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, Gemma 4 E2B achieves approximately 59.4 tokens per second decode speed with a time-to-first-token of 3259ms using Q4_K_M quantization.

Can GTX 1660 Super 6GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on GTX 1660 Super 6GB receives a A grade with 59.4 tok/s and 33K context.

What context window can Gemma 4 E2B use on GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, Gemma 4 E2B can safely use up to 33K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for GTX 1660 Super 6GB See all hardware for Gemma 4 E2B

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