Can Gemma 4 E4B run on RTX 5050 8GB?

YES — With Offload

A78Great

Estimated from fit model

Gemma 4 E4B needs ~7.9 GB VRAM. RTX 5050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~29 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 7.9 GB, 31.4 tok/s, Runs with offload

7.9 GB required8.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

31.4 tok/s

TTFT

6163 ms

Safe context

18K

Memory

7.9 GB / 8.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on RTX 5050 8GB
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: 31.4 tok/s decode · 6.2s TTFT (warm) · 79 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	29.2 tok/s	3614 ms	18K
Coding	A	Runs with offload	29.2 tok/s	6625 ms	18K
Agentic Coding	B	Very compromised	17.0 tok/s	16550 ms	18K
Reasoning	A	Runs with offload	29.2 tok/s	7830 ms	18K
RAG	B	Very compromised	17.0 tok/s	20688 ms	18K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on RTX 5050 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A81
Q3_K_S	3	3.9 GB	Low	A81
NVFP4	4

Get started

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

Run

ollama run gemma4:e4b

Your hardware

More models your RTX 5050 8GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	A	20.3 tok/s
CodeGeeX 4 9B	9B	A	37.5 tok/s

Frequently asked questions

Can RTX 5050 8GB run Gemma 4 E4B?

Yes, RTX 5050 8GB can run Gemma 4 E4B with a A grade (Runs with offload). Expected decode speed: 29.2 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 E4B?

The recommended quantization for Gemma 4 E4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 E4B run at on RTX 5050 8GB?

On RTX 5050 8GB, Gemma 4 E4B achieves approximately 29.2 tokens per second decode speed with a time-to-first-token of 6625ms using Q4_K_M quantization.

Can RTX 5050 8GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on RTX 5050 8GB receives a A grade with 29.2 tok/s and 18K context.

What context window can Gemma 4 E4B use on RTX 5050 8GB?

On RTX 5050 8GB, Gemma 4 E4B can safely use up to 18K tokens of context. 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 RTX 5050 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 5050 8GB See all hardware for Gemma 4 E4B

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