OSS + ELC EU 2026

Running a language model used to mean a datacenter GPU. This talk shows a 1-billion-parameter model generating tokens on a roughly $10 RISC-V board (the SOPHGO SG2002, as on the Sipeed LicheeRV Nano) with 256 MB of RAM no GPU, no Python, no cloud. The speaker walks the full embedded stack: a minimal C transformer inference engine running quantized GGUF weights, cross-compiled for RISC-V on embedded Linux, plus the quantization (4-bit GGUF), memory-mapping, and KV-cache choices that make it fit. The session is honest about the trade-offs, tokens-per-second, accuracy loss from aggressive quantization, and where an NPU or a microcontroller-class TinyML model is the better call. Attendees leave with a reproducible recipe for putting useful local inference on cheap, disconnected, privacy-sensitive hardware, and a clear-eyed sense of what 256 MB can and cannot do.