Can Gemma 4 E4B run on GTX 1060 6GB?

YES — With Q3_K_S

B65Good

Estimated from fit model

Gemma 4 E4B needs ~7.0 GB VRAM. GTX 1060 6GB has 6.0 GB. With Q3_K_S quantization, expect ~15 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: 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.

Gemma 4 E4B at Q4_K_M needs 8.0 GB — too much for GTX 1060 6GB (6.0 GB). Runs at Q3_K_S (7.0 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.0 GB, exceeds 6.0 GB available

8.0 GB required6.0 GB available

133% VRAM needed

2.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.8 tok/s

TTFT

19796 ms

Safe context

Memory

8.0 GB / 6.0 GB

Offload

20%

Memory breakdown

Weights4.9 GB

KV Cache1.3 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 4 E4B on GTX 1060 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: 9.8 tok/s decode · 19.8s TTFT (warm) · 24 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 0.6 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	F	Too heavy	11.8 tok/s	8981 ms	4K
Coding	F	Too heavy	9.8 tok/s	19796 ms	4K
Agentic Coding	F	Too heavy	7.0 tok/s	39964 ms	4K
Reasoning	F	Too heavy	9.8 tok/s	23396 ms	4K
RAG	F	Too heavy	7.0 tok/s	49955 ms	4K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on GTX 1060 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.1 GB	Low	A81
Q3_K_S	3	3.9 GB	Low	F0
NVFP4	4	4.5 GB	Medium	F0
Q4_K_M	4	4.9 GB	Medium	F0
Q5_K_M	5	5.8 GB	High	F0
Q6_K	6	6.6 GB	High	F0
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 4 E4B on your machine.

Run

ollama run gemma4:e4b

Upgrade options

Hardware that runs Gemma 4 E4B well

RTX 3050 8GBBudget pick

8 GB VRAM (+2)224 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.23.4 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.

~$249 MSRP

RTX 5060 8GBBest value

8 GB VRAM (+2)448 GB/s (+256)

Makes the model fit on the accelerator instead of staying completely out of reach.44.2 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.

~$299 MSRP

RTX 4060 8GBNVIDIA upgrade

8 GB VRAM (+2)272 GB/s (+80)

Makes the model fit on the accelerator instead of staying completely out of reach.31.5 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.

~$299 MSRP

RTX 3080 Ti 12GBBiggest leap

12 GB VRAM (+6)912 GB/s (+720)

Makes the model fit on the accelerator instead of staying completely out of reach.112 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.

~$1,199 MSRP

Frequently asked questions

Can GTX 1060 6GB run Gemma 4 E4B?

Yes, GTX 1060 6GB can run Gemma 4 E4B at Q3_K_S quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 8.0 GB which exceeds available memory, but at Q3_K_S it needs only 7.0 GB. Expected decode speed: 15.0 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 8.0 GB at Q4_K_M quantization. On GTX 1060 6GB, it fits at Q3_K_S using 7.0 GB.

What is the best quantization for Gemma 4 E4B?

The recommended quantization is Q4_K_M, but on GTX 1060 6GB the best fitting quantization is Q3_K_S, which uses 7.0 GB.

What speed will Gemma 4 E4B run at on GTX 1060 6GB?

On GTX 1060 6GB, Gemma 4 E4B achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12895ms using Q3_K_S quantization.

Can GTX 1060 6GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on GTX 1060 6GB receives a F grade with 9.8 tok/s and 4K context.

What context window can Gemma 4 E4B use on GTX 1060 6GB?

On GTX 1060 6GB, Gemma 4 E4B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 E4B feels slow on GTX 1060 6GB?

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.

See all results for GTX 1060 6GB See all hardware for Gemma 4 E4B

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Preview:

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 0.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

11.8 tok/s

8981 ms

Coding

Too heavy

9.8 tok/s

19796 ms

Agentic Coding

Too heavy

7.0 tok/s

39964 ms

Reasoning

Too heavy

9.8 tok/s

23396 ms

RAG

Too heavy

7.0 tok/s

49955 ms

Quant

Bits

VRAM

Quality

Fit

Q2_KBest for your GPU

3.1 GB

Low

A81

Q3_K_S

3.9 GB

Low

NVFP4

4.5 GB

Medium

Q4_K_M

4.9 GB

Medium

Q5_K_M

5.8 GB

High

Q6_K

6.6 GB

High

Q8_0

8.6 GB

Very High

F16

16.4 GB

Maximum

Frequently asked questions

Can GTX 1060 6GB run Gemma 4 E4B?

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 8.0 GB at Q4_K_M quantization. On GTX 1060 6GB, it fits at Q3_K_S using 7.0 GB.

What is the best quantization for Gemma 4 E4B?

The recommended quantization is Q4_K_M, but on GTX 1060 6GB the best fitting quantization is Q3_K_S, which uses 7.0 GB.

What speed will Gemma 4 E4B run at on GTX 1060 6GB?

On GTX 1060 6GB, Gemma 4 E4B achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12895ms using Q3_K_S quantization.

Can GTX 1060 6GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on GTX 1060 6GB receives a F grade with 9.8 tok/s and 4K context.

What context window can Gemma 4 E4B use on GTX 1060 6GB?

On GTX 1060 6GB, Gemma 4 E4B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 E4B feels slow on GTX 1060 6GB?

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.