Can I run Hy3 on a M5 Max 128GB Mac?
Computed with the open FitLLM engine — accurate per-layer KV-cache modeling, not a naive estimate. Updated 2026-07-10.
Memory breakdown (8-bit, F16 KV, 33K context)
| Model weights | 278 GB |
| KV cache | 10.0 GB |
| Runtime + macOS | 43.9 GB |
| Total used | 332 / 128 GB |
| Short by | 204 GB |
Max context at 8-bit: does not fit. Unified memory is shared by the OS — FitLLM leaves ~20% headroom.
Every quantization on M5 Max 128GB
| Quant | Weights | Fits (KV F16) | Used @32K |
|---|---|---|---|
| 4bit | 139 GB | ❌ won't fit | 176 / 128 GB |
| 8bit | 278 GB | ❌ won't fit | 332 / 128 GB |
| 16bit | 557 GB | ❌ won't fit | 644 / 128 GB |
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, never stale:
[](https://fitllm.run/can-i-run/hy3-on-m5-max-128gb)
Or from your terminal (exit 0/1 — works as a pre-download guard):
npx fitllm "Hy3" --mac 128Why 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 128GB: 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, Qwen3-0.6B, Qwen3-1.7B, Llama-3.2-1B-Instruct, Gemma-3-1B-it.
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