Gemma 4 VRAM Requirements: Every GPU and Mac Tested (2026)
Author: Gemma4Guide.com (no individual byline; topical reference site for Gemma 4) Format: Web reference article URL: https://gemma4guide.com/guides/gemma4-vram-requirements Published: 2026-04-05
Summary
A practical hardware reference for running Gemma 4 locally — model-by-model VRAM table, quantization breakdown, GPU lookup table, and Apple Silicon recommendations. Fills the long-standing dangling link [[gemma-4-vram-requirements]] referenced by 9 benchmark rig pages. See gemma-4-vram-requirements for the absorbed reference page.
Key Points
- Model weight sizes (BF16 baseline): E2B ~2 GB, E4B ~5 GB, 26B A4B ~14 GB, 31B ~24 GB. Add 1–3 GB runtime overhead.
- Q5_K_M is the recommended default — ~45% memory savings, very close to BF16 quality for most tasks
- 26B A4B is the standout — Mixture-of-Experts with only ~4B params active per inference; fits in 12–14 GB VRAM while delivering reasoning close to a dense 26B model
- GPU sweet spots: 12–16 GB → 26B A4B at Q5; 24 GB → 31B at Q5 or 26B A4B in BF16
- Apple Silicon caveat: macOS reserves ~4–6 GB for the OS — subtract before sizing. Best Mac experience: 32–36 GB unified memory running 26B A4B
- Mac M2/M3 Pro (18–36 GB) is the sweet spot for Mac local inference — workstation-class performance from a laptop
- Ollama uses Apple Metal automatically — no config
Sponsorship & Bias Notes
Sponsor: None disclosed. The site is a topical reference for Gemma 4, plausibly affiliated with the model community but not a Google property.
Product placement / affiliations: Mentions Unsloth on HuggingFace as the GGUF source — consistent with the broader local-LLM ecosystem; not a sponsored placement.
Comparison bias: None observed. The article is hardware-vs-model fit, not vendor comparison.
Connected Pages
- gemma-4-vram-requirements — full reference page absorbed from this source
- gemma-4 — model entity (updated with hardware section)
- index — rig pages that reference this for model fit
- ollama, llama-cpp, turboquant — runtime + quantization context
See Also
- Google Drops Gemma 4 — original launch coverage
- TurboQuant — KV cache optimization that extends the same hardware further