GoogleGemma 4 E4B on Apple M3 (8GB Unified)
M3 (8GB Unified) cannot run Gemma 4 E4B. 8 GB VRAM is insufficient at any quantization level.
Model Size
8B
Device VRAM
8 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 (8GB Unified) at Q8_0. This pairing has limitations — check the rating and notes below.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q8_0 | – | – | ✗ Offload | Not viable | estimated |
Notes
Q8_0
Smallest quantization requires more than the ~5.5GB available on this 8GB Mac.
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 (8GB Unified)
Apple M3 (8GB Unified) has 8 GB at 100 GB/s. Available in MacBook Air 13" (2024), MacBook Air 15" (2024).
See all models Apple M3 (8GB Unified) can run →Estimate method: Estimated: model exceeds 8GB Mac usable VRAM. 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.