GoogleGemma 4 E2B on Apple M2 Pro (16GB Unified)
Yes — M2 Pro (16GB Unified) handles Gemma 4 E2B well at Q8_0 — 24 tok/s. Solid daily-driver performance on 16 GB VRAM.
Model Size
5.1B
Device VRAM
16 GB
Bandwidth
200 GB/s
Quantization
Q8_0
Performance by Quantization
OwnRig currently has one published compatibility entry for Gemma 4 E2B on Apple M2 Pro (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 | 24 tok/s | – | ✓ Yes | Good | 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 E2B
Gemma 4 E2B (5.1B) is a chat, coding model. Gemma 4's compact edge model. 5.1B total parameters with 2.3B effective via Per-Layer Embeddings. Supports text, image, audio, and video input. Runs on practically any dedicated GPU with 4 GB of VRAM or more. Successor to Gemma 3 4B with measurably better reasoning and multimodal capabilities. Apache 2.0 licensed.
View all Gemma 4 E2B hardware options →About Apple M2 Pro (16GB Unified)
Apple M2 Pro (16GB Unified) has 16 GB at 200 GB/s. Available in MacBook Pro 14" (2023), MacBook Pro 16" (2023).
See all models Apple M2 Pro (16GB Unified) can run →Estimate method: Conservative estimate anchored to m3-pro-18gb 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.