Can Gemma 4 E4B run on RTX 2000 Ada 16GB?

YES — Runs Great

A79Great

Estimated from fit model

Gemma 4 E4B needs ~9.0 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~48 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) — 9.0 GB, 48.2 tok/s, Runs well

9.0 GB required16.0 GB available

56% VRAM used

Fit status

Runs well

Decode

48.2 tok/s

TTFT

4015 ms

Safe context

104K

Memory

9.0 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on RTX 2000 Ada 16GB
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: 48.2 tok/s decode · 4.0s TTFT (warm) · 121 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	48.2 tok/s	2190 ms	104K
Coding	A	Runs well	48.2 tok/s	4015 ms	104K
Agentic Coding	A	Runs well	48.2 tok/s	5840 ms	104K
Reasoning	A	Runs well	48.2 tok/s	4745 ms	104K
RAG	A	Runs well	48.2 tok/s	7300 ms	104K

Inference speed

Gemma 4 E4B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 4 E4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	99.7	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	93.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	83.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	82.6	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	77.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	73.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	62.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	52.9	Fits
RTX 3060 12GB	12 GB	Q4_K_M	52.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	48.5	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	42.6	Fits
RTX 4060 8GB	8 GB	Q4_K_M	31.5	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A74
Q3_K_S	3	3.9 GB	Low	A75
NVFP4	4	4.5 GB	Medium	A76
Q4_K_M	4	4.9 GB	Medium	A76
Q5_K_M	5	5.8 GB	High	A77
Q6_K	6	6.6 GB	High	A78
Q8_0Best for your GPU	8	8.6 GB	Very High	A79
F16	16	16.4 GB	Maximum	F0

Get started

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

Run

ollama run gemma4:e4b

Your hardware

More models your RTX 2000 Ada 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	42.9 tok/s
Qwen 3 14B	14B	S	27.7 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	26.2 tok/s
GPT-OSS 20B	21B	A	24.4 tok/s
Ministral 3 14B	14B	S	27.6 tok/s

Frequently asked questions

Can RTX 2000 Ada 16GB run Gemma 4 E4B?

Yes, RTX 2000 Ada 16GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 48.2 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 9.0 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 2000 Ada 16GB?

On RTX 2000 Ada 16GB, Gemma 4 E4B achieves approximately 48.2 tokens per second decode speed with a time-to-first-token of 4015ms using Q4_K_M quantization.

Can RTX 2000 Ada 16GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on RTX 2000 Ada 16GB receives a A grade with 48.2 tok/s and 104K context.

What context window can Gemma 4 E4B use on RTX 2000 Ada 16GB?

On RTX 2000 Ada 16GB, Gemma 4 E4B can safely use up to 104K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 2000 Ada 16GB See all hardware for Gemma 4 E4B

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