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.
Blockway says the v1 build and MIT engine aim to run a 32B model locally on consumer GPUs, while noting the architecture still needs its own SGLang/llama.cpp builds and cannot load in stock vLLM.
| https://reddit.com/link/1x8yc8d/video/hosf7c1n27wh1/player Hi r/LocalLLaMA. I'm on the team at Blockway (disclosure: this is our model). Two weeks ago we posted the Volundr 32B Preview here, and you were blunt about what was wrong with it. This is v1, and most of the work went into what you flagged. WHAT v1 FIXES FROM THE PREVIEW "MAKE IT RUN ON CONSUMER GPUS" → AGENS ENGINE (MIT) We're open-sourcing Agens Engine under the MIT licence. It's an inference engine built for this hybrid architecture: 54 of 72 layers keep a fixed-size state instead of a KV cache, and the engine is designed around that. STRENGTHS (same harness and settings as our comparison model) FULL TABLE (Preview → v1; dips included. Where we ran a benchmark more than once, we report the best of N runs. Every run is listed in the model card footnote.) A few scores went down (GPQA, HumanEval, IFBench). The HumanEval and IFBench dips are within one standard error of the runs; GPQA's isn't. That's the trade-off from tuning for agent behaviour and shorter reasoning. Harness settings are in the footnote under the results table: huggingface.co/Blockway/Agens-Volundr-32B-v1#results-same-harness-for-every-row Thanks to mmssix on Hugging Face for the independent benchmark write-up in our HF discussions. A lot of the above started there. KNOWN LIMITATIONS LINKS We'll be in the comments for the next couple of days. "Why doesn't X work yet" questions are welcome. https://preview.redd.it/4d8or6olv6wh1.png?width=2360&format=png&auto=webp&s=a83cbce77656ec8d9f28d9a3f9a5bfb8b96ce989 https://preview.redd.it/koyrx6olv6wh1.png?width=2360&format=png&auto=webp&s=6817372b70f3d4c7230fed90593c0a7015cbcc18 submitted by /u/ComfortableKindly507 [link] [comments] |
#Blockway#Agens Volundr 32B v1#Agens Engine#DFlash2#SGLang#llama.cpp#Qwen3.8-27B#Hugging Face