Can Gemma 4 E2B run on NVIDIA H100 80GB?

YES — Runs Great

B67Good

Estimated from fit model

Gemma 4 E2B needs ~12.8 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~71 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 12.8 GB, 71.4 tok/s, Runs well

12.8 GB required80.0 GB available

16% VRAM used

Fit status

Runs well

Decode

71.4 tok/s

TTFT

2711 ms

Safe context

128K

Memory

12.8 GB / 80.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on NVIDIA H100 80GB
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: 71.4 tok/s decode · 2.7s TTFT (warm) · 179 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	71.4 tok/s	3944 ms	128K
Reasoning	B	Runs well	71.4 tok/s	3204 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 H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	B62
Q3_K_S	3	2.5 GB	Low	B62
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 M3 Max 128GBBudget pick

128 GB Unified (+48)

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

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value

128 GB Unified (+48)

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

~$3,999 MSRP

Frequently asked questions

Can NVIDIA H100 80GB run Gemma 4 E2B?

Yes, NVIDIA H100 80GB 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 12.8 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 H100 80GB?

On NVIDIA H100 80GB, 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 H100 80GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on NVIDIA H100 80GB receives a B grade with 71.4 tok/s and 128K context.

What context window can Gemma 4 E2B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, 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 H100 80GB See all hardware for Gemma 4 E2B

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