Can Gemma 4 E2B run on NVIDIA L4 24GB?

YES — Runs Great

B70Good

Estimated from fit model

Gemma 4 E2B needs ~7.2 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~63 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 7.2 GB, 68.2 tok/s, Runs well

7.2 GB required24.0 GB available

30% VRAM used

Fit status

Runs well

Decode

68.2 tok/s

TTFT

2840 ms

Safe context

128K

Memory

7.2 GB / 24.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on NVIDIA L4 24GB
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: 68.2 tok/s decode · 2.8s TTFT (warm) · 170 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	68.2 tok/s	1549 ms	128K
Coding	B	Runs well	62.7 tok/s	3089 ms	128K
Agentic Coding	A	Runs well	68.2 tok/s	4131 ms	128K
Reasoning	A	Runs well	68.2 tok/s	3357 ms	128K
RAG	A	Runs well	68.2 tok/s	5164 ms	128K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	B67
Q3_K_S	3	2.5 GB	Low	B67
NVFP4	4	2.9 GB	Medium	B67
Q4_K_M	4	3.1 GB	Medium	B67
Q5_K_M	5	3.7 GB	High	B68
Q6_K	6	4.2 GB	High	B68
Q8_0	8	5.5 GB	Very High	B69
F16Best for your GPU	16	10.5 GB	Maximum	A72

Get started

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

Run

ollama run gemma4:e2b

升级选项

能流畅运行 Gemma 4 E2B 的硬件

MacBook Pro M4 Max 36GB经济之选

36 GB Unified (+12)410 GB/s (+110)

This setup is broadly balanced for this model.71.4 tok/s 解码

~$2,499 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Gemma 4 E2B?

Yes, NVIDIA L4 24GB can run Gemma 4 E2B with a B grade (Runs well). Expected decode speed: 62.7 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 7.2 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, Gemma 4 E2B achieves approximately 62.7 tokens per second decode speed with a time-to-first-token of 3089ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on NVIDIA L4 24GB receives a B grade with 62.7 tok/s and 128K context.

What context window can Gemma 4 E2B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Gemma 4 E2B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for Gemma 4 E2B

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