Gemma 4 26B-A4B on Apple M2 (8GB Unified)

M2 (8GB Unified) cannot run Gemma 4 26B-A4B. 8 GB VRAM is insufficient at any quantization level.

Model Size

25.2B

Device VRAM

8 GB

Bandwidth

100 GB/s

Quantization

Q3_K_M

Benchmarks

Performance by Quantization

OwnRig currently has one published compatibility entry for Gemma 4 26B-A4B on Apple M2 (8GB Unified) at Q3_K_M. This pairing has limitations — check the rating and notes below.

Quantization	Speed	TTFT	Fits in VRAM	Rating	Confidence
Q3_K_M	–	–	✗ Offload	Not viable	estimated

Notes

Q3_K_M

Smallest quantization requires more than the ~5.5GB available on this 8GB Mac.

About Gemma 4 26B-A4B

Gemma 4 26B-A4B (25.2B) is a chat, coding, reasoning, multi-purpose model. Mixture-of-Experts architecture: 25.2B total parameters but only 3.8B active per token (8 selected + 1 shared expert per layer, out of 128 total). Hybrid dense+sparse FFN design. Inference throughput closer to a 4B dense model; quality closer to a 27B dense model. 256K context window. Benchmarks: 88.3% AIME 2026, 82.6% MMLU Pro, 77.1% LiveCodeBench. All 25.2B weights must be loaded into VRAM despite sparse activation; fits on 24 GB GPUs at Q4_K_M. Apache 2.0 licensed.

View all Gemma 4 26B-A4B hardware options →

About Apple M2 (8GB Unified)

Apple M2 (8GB Unified) has 8 GB at 100 GB/s. Available in MacBook Air 13" (2022), MacBook Air 15" (2023), MacBook Pro 13" (2022).

See all models Apple M2 (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.