Can I run DeepSeek R1 1.5B?

DeepSeek R1 1.5B is a distilled reasoning model, and that word matters: it is tuned to think out loud through a problem rather than chat breezily. At 1.8B parameters it is tiny. A 4-bit quant lands around 1.1 GB, and even the 8-bit build is only 1.9 GB, so it fits in well under 3 GB of minimum RAM. That means it runs anywhere: an old laptop, a Raspberry Pi class board, a phone, or as a background helper that barely touches your GPU. Pull it with ollama run deepseek-r1:1.5b and you are going in seconds.

In daily use it is genuinely quick. On an RTX 3060 you can expect around 280 tokens per second, a 4090 pushes near 785, and an Apple M-series Max sits around 319, so the visible reply is gated more by how long it reasons than by raw speed. Even pure CPU on DDR5 manages roughly 47 tokens per second. The 128K context is the catch: this model burns tokens on its own chain-of-thought, and filling that window pushes total memory to about 9.7 GB, well past the idle footprint. Keep working context modest and let it think in short bursts.

Honestly, at this size the reasoning is hit or miss on anything multi-step; its larger siblings DeepSeek R1 7B and R1 8B are far more reliable when the answer actually matters, and Qwen 3 1.7B is the better pick if you mainly want plain chat rather than visible reasoning. Where the 1.5B wins is reach: it is the smallest way to see R1-style thinking traces run locally, and it does it on hardware nothing else will. The MIT license is the bonus, fully open and free to use commercially with no strings.