DeepSeek v4.1 Flash achieves 23 seconds per token on a 2020 16 GB M1 Mac Mini
deepseek huggingface training
| Source: HN | Original article
DeepSeek’s V4.1 Flash model processes tokens at roughly 23 seconds each on a 2020 16 GB M1 Mac Mini.
DeepSeek v4.1 Flash, the company’s latest vision‑language Mixture‑of‑Experts model, has been benchmarked on a 2020 Apple M1 Mac Mini with 16 GB of RAM, delivering roughly 23 seconds per generated token. The result, posted on a public GitHub repository, confirms that the 510 GB model can be downloaded and run on consumer‑grade hardware, albeit with a pronounced latency penalty.
The test matters because DeepSeek v4.1 Flash is one of the most demanding open‑source LLMs released this year. According to its Hugging Face page, the model was trained from scratch on a 45‑trillion‑token multimodal corpus, employs sparse attention at a 64 K sequence length, and can extend context to one million tokens after 34 T tokens of training. Its architecture comprises a 552‑billion‑parameter backbone, a 196‑billion‑parameter Engram memory, and a 32‑layer Vision Transformer front‑end, with active parameter counts of 8 B per prompt token and 16 B per output token. The GitHub notes that the download can be resumed if interrupted, a practical tip for the massive weight file.
Running the model locally on an M1 chip highlights the trade‑off between accessibility and performance. While the ability to execute a 552‑B‑parameter MoE on a modest Mac demonstrates the democratisation of cutting‑edge AI, the 23‑second token cost underscores the continued relevance of cloud‑based inference services. OrcaRouter already lists the model at $0.15–$0.60 per million tokens, and a recent YouTube demo explored the economics of local versus API deployment.
What to watch next: developers will likely experiment with quantisation, off‑loading, or hybrid cloud‑edge setups to shrink latency. DeepSeek’s pricing on routing platforms and any forthcoming optimisation releases will be key signals of whether such massive models can move beyond research labs into everyday applications. As we reported on 11 September 2026, DeepSeek v4.1 Flash Uncensored set new performance benchmarks; this Mac Mini test now adds a practical, if costly, perspective on its real‑world usability.
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