You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
llama.cpp fork for significantly improved performance on Ampere (especially RTX 3090 / 3090 Ti): example: 110+ tok/s over a 100K-token generation at temperature 1 for qwen3.8, supporting full 262K ctx. Supports low memory (12gb) configs for 200k+ ctx
A custom LLM inference engine for low-cost GPUs: NVIDIA cards from a 1 GB Quadro K420 to an RTX 3080, the AMD Radeon 780M and the CPU. Runs the Bonsai 2B to 27B models and Nemotron with an OpenAI-compatible server.
GLM-5.3-Flash on 4× NVIDIA CMP 170HX with vLLM: up to 482.6 tok/s single-user, up to 943.7 tok/s at 8 users, 262K context, TP4 or PP4, one-command container install
llama.cpp speculative decoding measured on one RTX 3090, Qwen3.6-35B-A3B UD-Q4_K_XL, commit 3737e4137. Published figures are re-derived from the committed data by a checker that fails on drift, a coverage probe reports how many of them it actually covers, and ERRATA.md lists this study's own retracted claims.
DACAN (Дацан): Qwen3.8-Flash-Next (125B MoE) on any NVIDIA RTX 20/30/40/50 card and any x86-64 CPU with AVX2, AVX-512 not required. One or two cards, one or two sockets, context up to 524K.
DeepSeek-V4.1-Flash on 4× NVIDIA CMP 170HX with vLLM: up to 319 tok/s for one user, up to 765 tok/s at eight, 262K context, one-command container install
We are a fosshost project which is delivering ARM-based hardware into multiple, global data centers. We document and keep a diary of our project daily.