Can Gemma 4 E4B run on RTX 2070 8GB?

YES — With Offload

A79Great

○Estimated from fit model

Gemma 4 E4B needs ~7.9 GB VRAM. RTX 2070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~45 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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) — 7.9 GB, 44.9 tok/s, Runs with offload

7.9 GB required8.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

44.9 tok/s

TTFT

4314 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 2070 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: 44.9 tok/s decode · 4.3s TTFT (warm) · 112 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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	44.9 tok/s	2353 ms	18K
Coding	A	Runs with offload	44.9 tok/s	4314 ms	18K
Agentic Coding	B	Very compromised (needs ~0.6 GB host RAM)	24.4 tok/s	11536 ms	18K
Reasoning	A	Runs with offload	44.9 tok/s	5099 ms	18K
RAG	B	Very compromised (needs ~0.6 GB host RAM)	24.4 tok/s	14420 ms	18K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on RTX 2070 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	4.5 GB	Medium	A80
Q4_K_MBest for your GPU	4	4.9 GB	Medium	A80
Q5_K_M	5	5.8 GB	High	F0
Q6_K	6	6.6 GB	High	F0
Q8_0	8	8.6 GB	Very High	F0
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 2070 8GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 9B	9B	A	27 tok/s
👁 Tsinghua/Zhipu CodeGeeX 4 9B	9B	A	53.6 tok/s

Frequently asked questions

See all results for RTX 2070 8GB See all hardware for Gemma 4 E4B

URL: https://willitrunai.com/can-run/gemma-4-e4b-on-rtx-2070-8gb