Can I run Hy3 on a M5 Max 64GB Mac?

Q: Can I run Hy3 on a M5 Max 64GB Mac?

No — at 32K context Hy3 (8-bit) needs about 332 GB of the 64 GB unified memory on a M5 Max 64GB Mac. Computed from the model's official config.json by the open-source FitLLM engine.

❌ Not at 32K context — Hy3 (8-bit) needs 332 GB of 64 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 (8.0 GB).

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

Model weights	278 GB
KV cache	10.0 GB
Runtime + macOS	43.9 GB
Total used	332 / 64 GB
Short by	268 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 M5 Max 64GB

Quant	Weights	Recommended max ctx	Used @32K
4bit	139 GB	won't fit	❌ 176 / 64 GB
8bit	278 GB	won't fit	❌ 332 / 64 GB
16bit	557 GB	won't fit	❌ 644 / 64 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: Hy3 on M5 Max 64GB Mac](https://img.shields.io/endpoint?url=https%3A%2F%2Ffitllm.run%2Fapi%2Fbadge%3Fmodel%3DHy3%26ram%3D64%26quant%3D8%26ctx%3D32768%26kv%3D16)](https://fitllm.run/can-i-run/hy3-on-m5-max-64gb)

fit badge preview ← renders like this, live.

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

npx fitllm "Hy3" --mac 64 --quant 8 --ctx 32768 --kv 16

Why most calculators get this wrong

Hy3 is a Mixture-of-Experts: ~21B of 298.8B parameters are active per token, but all 298.8B must sit in memory. Naive calculators that size memory off active params (or KV off all layers) get this wrong.

Other options

same Mac Models that fit in 64GB: GLM-4.7-Flash, gpt-oss-20b, Qwen 3.6 27B, Qwen 3.6 35B-A3B, Qwen-AgentWorld-35B-A3B, Gemma 4 e2b, Gemma 4 e4b, Gemma 4 12b, Gemma 4 26b A4B, Gemma 4 31b, Llama-3.2-3B-Instruct, Llama-3.1-8B-Instruct, MiniCPM5-1B, Qwen3-0.6B, Qwen3-1.7B, Llama-3.2-1B-Instruct, Gemma-3-1B-it, Qwen 3.8 27B, Laguna XS 2.1, Spark-X2.5-4B, Granite-4.2-30B, MiniCPM5-2B, Nex-N2.5-mini.

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