Agens Engine

Headlines · 2

1. Oct 19
   Blockway's Agens Engine keeps a 32B model's 262K context within a 16GB GPU budget

   Blockway, a small team from Hong Kong, says 54 of the 72 layers in its 32B Volundr model keep a small fixed-size state instead of a KV cache that grows with every token, and that it built its own MIT-licensed Agens Engine so long context can spill to system RAM within a chosen GPU memory budget. In an uncut demo, a roughly 110K-token novel reads cold in about 69 seconds and a follow-up is answered in 0.5 seconds, with GPU memory peaking around 15.6 GiB; the team discloses that the run was recorded on a 48GB card with the engine held to a 16 GiB plan because it does not yet own a real 16GB card. Blockway also cites Hugging Face user mmssix, who measured 86.4–106.7 tok/s on a 24GB card, and lists an MoE variant, same-GPU image generation, Windows and macOS support, and a compact desktop as future work.

   r/LocalLLaMA · 🔥 0
2. Oct 18
   Blockway releases Agens Volundr 32B v1 with an MIT-licensed Agens Engine

   Blockway posted Agens Volundr 32B v1 on r/LocalLLaMA, saying it addressed issues the community raised against the Preview: repetition loops on SWE-bench tasks fell from 36/50 to 0/50, SWE-bench Verified (50-task subset) rose from 22/50 to 27/50, τ²-bench from 89 to 92/120, and median SWE task time dropped from 124 to 82 minutes. The team also open-sourced Agens Engine under the MIT licence, an inference engine built for the hybrid architecture, claiming 1.4–1.6× faster single-stream decoding than its own SGLang build, plus a roughly 2.85 bits-per-weight build offering 262K context within a 16 GB memory budget. All figures are vendor self-reported: the 16 GB numbers were measured on a 48 GB card under a 16 GiB cap, GPQA drops to 73.7 at that bit-width versus 80.3 at bf16, and the team says stock vLLM cannot load the architecture while SGLang and llama.cpp still require its own builds.

   r/LocalLLaMA · 🔥 0

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