Qwen3.8 is a version of a large language model. Recent coverage notes a DIY ~$3k inference server running Qwen3.8-next (4×V620, 128GB VRAM, EPYC CPU) with usable performance but 500–900W power draw; community discussion about applying KVCache/Engram to let 30B-class models fit smaller GPUs; and comparisons with models like CyberTiel and Opus on coding tasks.
Qwen3.8 27b game dev postA Reddit post discusses using Qwen3.8 27b in game development, noting the model can't produce 3D/GLB files but can assist once assets are added to a game repo. It's a developer experience share, not a formal release.
User builds $3k inference server with 128GB VRAMA Reddit user posted a DIY inference server built for about $3k featuring 4×V620 GPUs (128GB VRAM), 256GB DDR4 RAM, EPYC 7452 CPU, and a Samsung 970 EVO SSD; it runs Qwen3.8-next with usable performance but high power draw (500–900W).
Hope for KVCache + Engram in Upcoming ModelsA Reddit post urges applying DeepSeek-V4.1-Flash’s KVCache and Engram ideas to medium/large models so 30B-class models can run on GPUs in Q8/Q4. The post gives approximate VRAM and Engram estimates, suggests Engram may be ~1/3–1/2 of model size (10–15 GB for 30B), and argues 32GB VRAM should suffice for many optimized models.
CyberTiel 35B-A3B uncensored quant excels at codingAn anonymous r/LocalLLaMA post claims the open-source CyberTiel 35B-A3B, after 4-bit quantization and uncensoring, outperforms Opus and Qwen on real codebase tasks, preserving key weights while enabling more security research and user control.
The A.I. Daily