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Yes. DeepSeek Coder V2 Lite 16B (MoE) fits in fast memory on M1 Pro 16GB at Q4_0 (9.9 GB needed, 10.7 GB usable), about 59 tok/s (estimate).
Hardware: M1 Pro 16GB · Model: DeepSeek Coder V2 Lite 16B (MoE)
M1 Pro 16GB has 16 GB of unified memory and 200 GB/s of memory bandwidth. M2 Pro 16GB has the same memory and bandwidth, so the fit numbers match; this page is M1 Pro 16GB. The checker leaves macOS a share of unified memory, so Ollama can use 10.7 GB. DeepSeek Coder V2 Lite 16B (MoE) (15.7B, 2.4B active, DeepSeek) at Q4_0 is a 8.9 GB download and needs 9.9 GB with headroom for about 8k tokens.
| Best quant | Q4_0 |
|---|---|
| Download size | 8.9 GB |
| Memory needed | 9.9 GB |
| Usable fast memory | 10.7 GB |
| Fit | fits in fast memory |
| Estimated speed | ~59 tok/s (estimate) |
| Command | ollama run deepseek-coder-v2:16b |
Estimates, not benchmarks. Model sizes are ollama.com download sizes; memory needed adds headroom for ~8k tokens of context; speeds are rough figures from memory bandwidth. Data updated 2026-10-23.
A project by OnDevice Labs · llms.txt