Can I run Gemma 4 12b on a M6 16GB Mac?

Q: Can I run Gemma 4 12b on a M6 16GB Mac?

No — at 32K context Gemma 4 12b (8-bit) needs about 23.4 GB of the 16 GB unified memory on a M6 16GB Mac. Computed from the model's official config.json by the open-source FitLLM engine.

❌ Not at 32K context — Gemma 4 12b (8-bit) needs 23.4 GB of 16 GB unified memory

Computed with the open FitLLM engine — accurate per-layer KV-cache modeling, not a naive estimate. Updated 2026-10-19.

Conditions: 8-bit · KV cache F16 · 32K context · macOS reserve (9.0 GB).

Memory breakdown (8-bit, F16 KV, 32K context)

Model weights	11.1 GB
KV cache	0.8 GB
Runtime + macOS	11.4 GB
Total used	23.4 / 16 GB
Short by	7.4 GB

Context at 8-bit: does not fit at any context. Unified memory is shared with macOS and your apps — the recommended max leaves 20% of it free.

Every quantization on M6 16GB

Quant	Weights	Recommended max ctx	Hard limit	Used @32K
4bit	5.6 GB	—	~9K	❌ 17.1 / 16 GB
8bit	11.1 GB	won't fit		❌ 23.4 / 16 GB
16bit	22.3 GB	won't fit		❌ 35.8 / 16 GB

Recommended max = the longest context that still keeps the 20% headroom macOS needs · hard limit = the longest that fits at all (KV cache F16).

Lower quants free memory at some output-quality cost — 4-bit is the common sweet spot for local use.

▶ Open the interactive calculator (this exact setup)

Embed this verdict

Live badge for your README or model card — recomputed by the engine at exactly these conditions, never stale:

[![fits: Gemma 4 12b on M6 16GB Mac](https://img.shields.io/endpoint?url=https%3A%2F%2Ffitllm.run%2Fapi%2Fbadge%3Fmodel%3DGemma%2B4%2B12b%26ram%3D16%26quant%3D8%26ctx%3D32768%26kv%3D16)](https://fitllm.run/can-i-run/gemma-4-12b-on-m6-16gb)

fit badge preview ← renders like this, live.

Or from your terminal (exit 0/1 — works as a pre-download guard):

npx fitllm "Gemma 4 12b" --mac 16 --quant 8 --ctx 32768 --kv 16

Why most calculators get this wrong

Gemma 4 12b interleaves sliding-window (local) and global attention 5:1. The local layers cap their KV cache at the 1024-token window, and the global layers use a different head shape (head_dim 512 vs 256). A naive "all layers × full context × one head_dim" formula over-counts KV cache by several times.

Other options

same Mac Models that fit in 16GB: MiniCPM5-1B, Qwen3-0.6B, Qwen3-1.7B, Llama-3.2-1B-Instruct, Gemma-3-1B-it, MiniCPM5-2B.

Reproduce it

Open math: fitllm-engine (MIT), from official config.json.

All numbers are computed by the open-source fitllm-engine (MIT) from official model config.json values — reproduce or audit them yourself. Estimates; real usage varies with runtime (llama.cpp / MLX / Ollama), driver and display. Found a mismatch? Report it. · FitLLM home