# Can I run R1 Distill 14B on a Mac mini 16GB? > Yes, but it is tight. R1 Distill 14B at Q4_K_M needs about 11.3 GB; Mac mini 16GB has 12.0 GB usable. Estimated tokens/sec and context headroom. Source: https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-14b/mac-mini-m6-16/ [Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [R1 Distill 14B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-14b/) / R1 Distill 14B on a Mac mini 16GB **Yes, but it is tight.** R1 Distill 14B at Q4\_K\_M needs about 11.3 GB, and the Mac mini M6 16GB has 12.0 GB usable at a \~4k context. # Can I run R1 Distill 14B on a Mac mini 16GB? R1 Distill 14B at Q4\_K\_M needs about 11.3 GB, and the Mac mini M6 16GB has 12.0 GB usable. That figure is 16 GB of unified memory on the spec sheet, of which 12.0 GB is usable for weights and cache, at 153 GB/s memory bandwidth. A 14B DeepSeek-R1 distill on Qwen. Same memory class as other 14B dense models, with reasoning-tuned weights. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks. Quant that fits Q4\_K\_M Fits, short context only Memory needed 11.3 GB at Q4\_K\_M, \~4k context Usable unified memory 12.0 GB of 16 GB unified memory Est. tokens/sec 8–14 estimate [DeepSeek-R1-Distill-Qwen-14B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-14b/) · [Mac mini M6 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-16/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/mac-mini-m6-16/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/) ## Memory at each quant | Quant | Need vs 12.0 GB | Verdict | | -------- | --------------- | ------------- | | Q4\_K\_M | 11.3 GB | Q4Tight | | Q8\_0 | 19.0 GB | Q8Won’t fit | | FP16 | 35.0 GB | FP16Won’t fit | ## Estimated tokens per second Estimate: **8–14 tokens/sec** at Q4\_K\_M. This is a bandwidth-derived range, not a bench. Efficiency is a 45–80% unified-memory band from [How we calculate](https://whatcanirunai.vercel.app/method/). ## Context headroom At Q4\_K\_M, this machine has headroom out to about **4k tokens** under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache. __Context headroom for R1 Distill 14B on a Mac mini 16GB at Q4\_K\_M__ | Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict | | ------- | --------------- | ----------------- | --------- | | 4k | 0.8 GB | 11.3 GB | Tight | | 8k | 1.6 GB | 12.1 GB | Won’t fit | | 16k | 3.2 GB | 13.7 GB | Won’t fit | | 32k | 6.4 GB | 16.9 GB | Won’t fit | | 64k | 12.9 GB | 23.4 GB | Won’t fit | | 128k | 25.8 GB | 36.3 GB | Won’t fit | From the model’s config. Try other sizes in the [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) or see the [context length guide](https://whatcanirunai.vercel.app/context-length/). ## What to do instead * Run [Qwen3-14B](https://whatcanirunai.vercel.app/can-i-run/qwen3-14b/mac-mini-m6-16/) instead (11.3 GB at Q4). It is a different model, not a drop-in, but it does fit this machine. * Q8\_0 needs 19.0 GB and does not fit here. The [Radeon RX 7900 XT](https://whatcanirunai.vercel.app/hardware/rx-7900-xt/) has 20.0 GB usable, enough for Q8. * See the [best GPU for this model family](https://whatcanirunai.vercel.app/best-gpu-for/deepseek-r1/) and the [DeepSeek-R1-Distill-Qwen-14B model page](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-14b/). ## Other hardware for R1 Distill 14B * [R1 Distill 14B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-14b/rtx-3060-12gb/)Tight * [R1 Distill 14B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-14b/rtx-3070-ti/)Won’t fit * [R1 Distill 14B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-14b/rtx-4090/)Fits * [R1 Distill 14B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-14b/arc-b580/)Tight ## Other models on a Mac mini 16GB * [Qwen3-32B on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/mac-mini-m6-16/)Won’t fit * [Qwen3-30B-A3B on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/mac-mini-m6-16/)Won’t fit * [Qwen3.8-27B on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/mac-mini-m6-16/)Won’t fit * [Gemma 3 27B on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/mac-mini-m6-16/)Won’t fit * [Gemma 4 31B on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/mac-mini-m6-16/)Won’t fit * [gpt-oss-20b on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/mac-mini-m6-16/)Won’t fit ## Quick answers Can I run R1 Distill 14B on a Mac mini 16GB? Yes, but it is tight. R1 Distill 14B at Q4\_K\_M needs about 11.3 GB, and the Mac mini M6 16GB has 12.0 GB usable. How much VRAM does R1 Distill 14B need? About 11.3 GB at Q4\_K\_M, 19.0 GB at Q8\_0 and 35.0 GB at FP16, estimated for a \~4k context. How fast is R1 Distill 14B on a Mac mini 16GB? Our estimate is 8–14 tokens per second at Q4\_K\_M. It is not a measured benchmark. What context length can I use? At Q4\_K\_M, our KV estimate supports up to about 4k tokens before the cache pushes the total over usable memory. --- Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/