Can Gemma 4 E2B run on NVIDIA L20 48GB?

YES — Runs Great

B68Good

Estimated from fit model

Gemma 4 E2B needs ~9.3 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~71 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 9.3 GB, 81.6 tok/s, Runs well

9.3 GB required48.0 GB available

19% VRAM used

Fit status

Runs well

Decode

81.6 tok/s

TTFT

2373 ms

Safe context

128K

Memory

9.3 GB / 48.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on NVIDIA L20 48GB
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: 81.6 tok/s decode · 2.4s TTFT (warm) · 204 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	B	Runs well	71.4 tok/s	1479 ms	128K
Coding	B	Runs well	71.4 tok/s	2711 ms	128K
Agentic Coding	B	Runs well	81.6 tok/s	3451 ms	128K
Reasoning	B	Runs well	81.6 tok/s	2804 ms	128K
RAG	B	Runs well	71.4 tok/s	4930 ms	128K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	B64
Q3_K_S	3	2.5 GB	Low	B64
NVFP4	4

Get started

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

Run

ollama run gemma4:e2b

Upgrade options

Hardware that runs Gemma 4 E2B well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.71.4 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

96 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.71.4 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Frequently asked questions

Can NVIDIA L20 48GB run Gemma 4 E2B?

Yes, NVIDIA L20 48GB can run Gemma 4 E2B with a B grade (Runs well). Expected decode speed: 71.4 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 9.3 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 L20 48GB?

On NVIDIA L20 48GB, Gemma 4 E2B achieves approximately 71.4 tokens per second decode speed with a time-to-first-token of 2711ms using Q4_K_M quantization.

Can NVIDIA L20 48GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on NVIDIA L20 48GB receives a B grade with 71.4 tok/s and 128K context.

What context window can Gemma 4 E2B use on NVIDIA L20 48GB?

On NVIDIA L20 48GB, 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 L20 48GB See all hardware for Gemma 4 E2B

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