The team from Hong Kong developed a custom inference engine called Volundr that enables a 32‑billion‑parameter model to maintain a 262 K token context while staying within a 16 GB GPU memory budget. By reducing KV‑cache growth, the engine avoids the need to truncate context or offload to cloud services during long agent sessions. This approach allows extended, on‑device interactions with large models.