Agens Volundr 32B v1: we fixed what you flagged in the Preview. 0/50 loops, session continuation, and an MIT engine that runs it with 262K context (measured to 239K) within a 16 GB budget

Key points

• Repetition loops on SWE-bench tasks: 36/50 → 0/50.1
• "No prefix caching, it re-reads the whole history every turn." - SGLang build: v1 adds session continuation, so follow-up turns reuse the model's state instead of re-prefilling.1
• GGUF: uses llama-server's stock --ctx-checkpoints prompt cache, with no custom server code.1
• Same GPU, same v1 INT4 weights and drafter, single stream: Agens Engine decodes 1.4–1.6× faster than our own SGLang build (up to 1.8× on code with thinking).1

Sources (1)

• [1]Agens Volundr 32B v1: we fixed what you flagged in the Preview. 0/50 loops, session continuation, and an MIT engine that runs it with 262K context (measured to 239K) within a 16 GB budget
  r/LocalLLaMA (top, daily) · Oct 18, 08:49 AM

  WHAT v1 FIXES FROM THE PREVIEW

  Repetition loops on SWE-bench tasks: 36/50 → 0/50.

Extractive summary: sentences quoted from the sources.