Can Gemma 4 E2B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

B67Good

Estimated from fit model

Gemma 4 E2B needs ~17.9 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~57 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) — 17.9 GB, 57.3 tok/s, Runs well

17.9 GB required108.8 GB available

16% VRAM used

Fit status

Runs well

Decode

57.3 tok/s

TTFT

3381 ms

Safe context

128K

Memory

17.9 GB / 108.8 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on NVIDIA DGX Spark 128GB
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: 57.3 tok/s decode · 3.4s TTFT (warm) · 143 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	57.3 tok/s	1844 ms	128K
Coding	B	Runs well	57.3 tok/s	3381 ms	128K
Agentic Coding	B	Runs well	57.3 tok/s	4918 ms	128K
Reasoning	B	Runs well	57.3 tok/s	3996 ms	128K
RAG	B	Runs well	57.3 tok/s	6148 ms	128K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 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	2.9 GB	Medium	B62
Q4_K_M	4	3.1 GB	Medium	B62
Q5_K_M	5	3.7 GB	High	B62
Q6_K	6	4.2 GB	High	B62
Q8_0	8	5.5 GB	Very High	B62
F16Best for your GPU	16	10.5 GB	Maximum	B62

Get started

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

Run

ollama run gemma4:e2b

Opciones de mejora

Hardware que ejecuta bien Gemma 4 E2B

NVIDIA H200 141GBOpción económica

141 GB VRAM (+13)4800 GB/s (+4527)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.71.4 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

NVIDIA H200 PCIe 141GBMejor relación calidad-precio

141 GB VRAM (+13)4800 GB/s (+4527)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.71.4 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Gemma 4 E2B?

Yes, NVIDIA DGX Spark 128GB can run Gemma 4 E2B with a B grade (Runs well). Expected decode speed: 57.3 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 17.9 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 DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Gemma 4 E2B achieves approximately 57.3 tokens per second decode speed with a time-to-first-token of 3381ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on NVIDIA DGX Spark 128GB receives a B grade with 57.3 tok/s and 128K context.

What context window can Gemma 4 E2B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, 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.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Gemma 4 E2B?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GB See all hardware for Gemma 4 E2B

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