GoogleGemma 4 E4B on Apple M3 (16GB Unified)
M3 (16GB Unified) runs Gemma 4 E4B at Q8_0 — 6 tok/s due to limited memory bandwidth. Slow but functional — see all options below.
Model Size
8B
Device VRAM
16 GB
Bandwidth
100 GB/s
Quantization
Q8_0
Performance by Quantization
OwnRig currently has one published compatibility entry for Gemma 4 E4B on Apple M3 (16GB Unified) at Q8_0. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q8_0 | 6 tok/s | – | ✓ Yes | Marginal | estimated |
Notes
Q8_0
Conservative estimate anchored to existing Apple Silicon results. Memory bandwidth and quantization size are the primary inference limiters.
About Gemma 4 E4B
Gemma 4 E4B (8B) is a chat, coding, reasoning model. Gemma 4's mid-range edge model. 8B total parameters with 4.5B effective. Full multimodal: text, image, audio, and video. 52% LiveCodeBench v6 and 42.5% AIME 2026 put its reasoning and coding above most models at this size. Fits comfortably on 8 GB GPUs at Q4_K_M. Apache 2.0 licensed.
View all Gemma 4 E4B hardware options →About Apple M3 (16GB Unified)
Apple M3 (16GB Unified) has 16 GB at 100 GB/s. Available in MacBook Air 13" (2024), MacBook Air 15" (2024).
See all models Apple M3 (16GB Unified) can run →Estimate method: Conservative estimate anchored to m4-16gb with bandwidth scaling, quantization-size adjustment, and generation damping. Reference hardware source: github.com (2026-04-18)
Performance varies by driver version, inference engine, quantization method, context length, and system configuration. Figures shown are estimates based on community benchmarks and may not reflect your exact setup. Product names are trademarks of their respective owners. OwnRig is independent and not affiliated with any hardware or AI model provider.