Can Gemma 4 E2B run on RX 6600 8GB?

YES — Runs Great

A77Great

Estimated from fit model

Gemma 4 E2B needs ~5.6 GB VRAM. RX 6600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: 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) — 5.6 GB, 38.4 tok/s, Runs well

5.6 GB required8.0 GB available

70% VRAM used

Fit status

Runs well

Decode

38.4 tok/s

TTFT

5046 ms

Safe context

87K

Memory

5.6 GB / 8.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on RX 6600 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: 38.4 tok/s decode · 5.0s TTFT (warm) · 96 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	38.4 tok/s	2752 ms	87K
Coding	A	Runs well	38.4 tok/s	5046 ms	87K
Agentic Coding	A	Runs well	38.4 tok/s	7339 ms	87K
Reasoning	A	Runs well	38.4 tok/s	5963 ms	87K
RAG	A	Runs well	38.4 tok/s	9174 ms	87K

Inference speed

Gemma 4 E2B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 4 E2B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~97 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	96.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	81.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	81.6	Fits
RTX 3090 24GB	24 GB	Q4_K_M	71.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	71.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	71.4	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	71.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	71.4	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	71.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	71.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	71.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	71.4	Fits
RTX 4060 8GB	8 GB	Q4_K_M	69.4	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	67.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	63.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	58.3	Fits

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 E2B (5.099999904632568B params) fits at each quantization level on RX 6600 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	A75
Q3_K_S	3	2.5 GB	Low	A76
NVFP4	4	2.9 GB	Medium	A76
Q4_K_M	4	3.1 GB	Medium	A77
Q5_K_M	5	3.7 GB	High	A76
Q6_KBest for your GPU	6	4.2 GB	High	A76
Q8_0	8	5.5 GB	Very High	F0
F16	16	10.5 GB	Maximum	F0

Get started

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

Run

ollama run gemma4:e2b

Your hardware

More models your RX 6600 8GB can run

Model	Params	Grade	Decode
Qwen 3 8B	8B	A	13.9 tok/s
Nemotron Nano 8B	8B	A	14.7 tok/s
InternVL2 8B	8B	A	14.7 tok/s
Ministral 3 8B	8B	B	13.9 tok/s
MiniCPM-V 2.6 8B	8B	B	14.7 tok/s

Frequently asked questions

Can RX 6600 8GB run Gemma 4 E2B?

Yes, RX 6600 8GB can run Gemma 4 E2B with a A grade (Runs well). Expected decode speed: 38.4 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 5.6 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 RX 6600 8GB?

On RX 6600 8GB, Gemma 4 E2B achieves approximately 38.4 tokens per second decode speed with a time-to-first-token of 5046ms using Q4_K_M quantization.

Can RX 6600 8GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on RX 6600 8GB receives a A grade with 38.4 tok/s and 87K context.

What context window can Gemma 4 E2B use on RX 6600 8GB?

On RX 6600 8GB, Gemma 4 E2B can safely use up to 87K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RX 6600 8GB See all hardware for Gemma 4 E2B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/gemma-4-e2b-on-rx-6600-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

38.4 tok/s

2752 ms

87K

Coding

Runs well

38.4 tok/s

5046 ms

87K

Agentic Coding

Runs well

38.4 tok/s

7339 ms

87K

Reasoning

Runs well

38.4 tok/s

5963 ms

87K

RAG

Runs well

38.4 tok/s

9174 ms

87K

Gemma 4 E2B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

96.9

Fits

RTX 4090 24GB

24 GB

Q4_K_M

81.6

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

81.6

Fits

RTX 3090 24GB

24 GB

Q4_K_M

71.4

Fits

RTX 4070 12GB

12 GB

Q4_K_M

71.4

Fits

RTX 3060 12GB

12 GB

Q4_K_M

71.4

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

71.4

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

71.4

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

71.4

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

71.4

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

71.4

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

71.4

Fits

RTX 4060 8GB

8 GB

Q4_K_M

69.4

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

67.6

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

63.6

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

58.3

Fits

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on RX 6600 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	A75
Q3_K_S	3	2.5 GB	Low	A76
NVFP4	4	2.9 GB	Medium	A76
Q4_K_M	4	3.1 GB	Medium	A77
Q5_K_M	5	3.7 GB	High	A76
Q6_KBest for your GPU	6	4.2 GB	High	A76
Q8_0	8	5.5 GB	Very High	F0
F16	16	10.5 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

13.9 tok/s

14.7 tok/s

14.7 tok/s

13.9 tok/s

14.7 tok/s

Frequently asked questions

Can RX 6600 8GB run Gemma 4 E2B?

Yes, RX 6600 8GB can run Gemma 4 E2B with a A grade (Runs well). Expected decode speed: 38.4 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 5.6 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 RX 6600 8GB?

On RX 6600 8GB, Gemma 4 E2B achieves approximately 38.4 tokens per second decode speed with a time-to-first-token of 5046ms using Q4_K_M quantization.

Can RX 6600 8GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on RX 6600 8GB receives a A grade with 38.4 tok/s and 87K context.

What context window can Gemma 4 E2B use on RX 6600 8GB?

On RX 6600 8GB, Gemma 4 E2B can safely use up to 87K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.