DeepSeek V4.1-Flash separates model capacity from active compute using sparse Engram memory and tiered storage, pointing toward a practical blueprint for running very large models locally.
Adapted from @TeksEdgeDeepSeek-V4.1-Flash may be showing us where Local AI architecture is heading. Like Qwen3.8-Flash-Next, it’s another sign that model capacity is becoming increasingly decoupled from active compute. 🎯 It may be a blueprint for how MUCH bigger AI models eventually run locally. DeepSeek's new V4.1 separated the model into different kinds of memory + compute such as... 🧠 552B backbone 📚 196B Engram memory ⚡ only 8B active/token during input 🚀 only 16B active/token during output The 196B Engram is especially interesting. Instead of behaving like normal model weights that all participate in matrix math, it's sparsely accessed through token-based lookup. For Local AI, architectures like this suggest a memory hierarchy that could look like ... 🎮 accelerator/unified memory → hot compute 💾 host/unified memory → resident weights 💽 SSD → sparse tables / cold parameters A community 2-bit MLX build on a 256GB M3 Ultra kept ... 🧠 160.9 GiB backbone in RAM 💽 57.2 GiB Engram tables on SSD ...and produced short text generations at around 8.8–9.5 tps. ⚠️ This is VERY experimental and not a production benchmark or stock MLX. ... and DeepSeek attacked another Local AI bottleneck too, KV cache. V4.1 Flash's global KV cache is only 890 bytes/token. At 1M tokens, that's roughly 890MB of global KV. Small active compute. Huge cheap memory. SSD-backed knowledge. Tiny KV cache. VRAM may be becoming just one tier in the Local AI memory hierarchy. 👀🔥
