Can I run it?
Can I run Hy MT2 1.8B on Apple M5?
Runs great
Runs great fully in unified memory on your Apple M5 at Q8_0, 8K context, about 34–47 tok/s. Weights 1.78 GB plus 0.50 GB of KV cache — 2.60 GB of 11.0 GB usable. Up to 128K context still fits.
Best quantQ8_0
Working context8K
Max that fits128K
Speed34–47 tok/s
Weights1.78 GB
KV cache0.50 GB
Total2.60 GB
Usable11.0 GB
Hy MT2 1.8B details · Apple M5 page · How this estimate is made
Run commands
llama.cpp
llama-server -hf tencent/Hy-MT2-1.8B-GGUF:Q8_0 -c 8192 -ngl 32
No matching name in the Ollama library snapshot.
LM Studio
lms get tencent/Hy-MT2-1.8B-GGUF # Then load quant Q8_0
Assumptions
- Estimate, not a benchmark. Decode speed uses a memory-bandwidth model at 55–75% of peak.
- KV cache is FP16. Engine overhead assumes llama.cpp (0.3 GB + 1% of the weights). Ollama is closer to 0.9 GB + 3%, LM Studio closer to 1 GB + 3%.
- Q8_0 size is the real GGUF file size from Hugging Face.
- Unified memory uses 75% for the GPU by default, and always leaves about 5 GB for the OS.
- Speed is quoted at 8K context, not at the maximum context that still fits.
Larger models that also fit
- Can I run Qwen3.8 27B on Apple M5? — Tight fit, UD-Q2_K_XL, 8.1–11 tok/s
- Can I run gemma 4 12b it on Apple M5? — Tight fit, Q6_K, 6.5–8.8 tok/s
- Can I run Qwen3.5 9B on Apple M5? — Tight fit, Q8_0, 8.6–12 tok/s
Updated 2026-10-24. Profile on this page is the device default.