πŸŸ₯ Betelgeuse-AI (Alpha-Orionis)

Out-of-core frontier MoE inference and pre-packed streaming bundles for consumer hardware (12 GB VRAM + 16 GB RAM).

Betelgeuse-AI builds bel-cli / bel-server and pre-split GGUF + contiguous expert-pack bundles designed to run 70B–125B+ Mixture-of-Experts models at interactive speeds directly off consumer NVMe SSDsβ€”with zero gate pruning, zero dropped experts, and 100% byte-exact output parity.


⚑ Flagship Release: Betelgeuse-AI/Alpha-Orionis-XS

A zero-duplication, streaming-ready bundle of Qwen 3.8 Flash Next (125B MoE, 48 layers, 512 experts/layer, top-10 routed, GSQ-RCO IQ2_XS):

Artifact Size Role
Qwen3.8-Flash-Next-Dense.gguf 3.2 GB Dense backbone (Attention, 36 Gated DeltaNet layers, 12 QSA layers, Shared Experts, Routers, LM Head, Tokenizer) β€” resident in VRAM
experts.bin 35.3 GB All 24,576 routed experts pre-packed & 4096-byte aligned ([gate | up | down]) for single-read io_uring streaming
Qwen3.8-Flash-Next-PLE.gguf 26.8 GB Per-layer token embedding (per_layer_token_embd.weight) shard, active rows staged to CUDA0
profile-qwen4exp.bin 10 KB Pre-computed arrival-slack hot expert profile

πŸ“Š Verified Benchmarks (RTX 3060 12 GB + 16 GB Single-Channel DDR4, MemoryMax=12G)

Runtime Cold Decode (Run 1) Warm Decode (Run 2) Peak Bursts Prompt Eval (PP) Physical NVMe Reads Output Parity
Stock llama.cpp (mmap) 1.47 tok/s 1.63 tok/s ~2.0 tok/s 14.1 – 20.8 tok/s 208 – 312 MiB/tok Reference
Strata (--low-ram mmap) 8.11 tok/s 8.11 tok/s ~9.0 tok/s 34.6 tok/s ~105 ms pool wait ~0.022 KL
Betelgeuse (bel-server) 16.80 tok/s 22.03 tok/s Up to 48 tok/s 16.2 – 19.4 tok/s 12.43 MiB/tok 100% Byte-Exact

πŸ”­ Roadmap