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Intel Arc Pro B65 four-GPU rig posts long-context throughput sweeps for Qwen3.8 models
8 Flash-Next and 27B models on 1x to 4x Intel B65 setups with vLLM, reporting output throughput across context lengths up to 200K. Results include queueing effects and are not model-quality scores.
Paper introduces COUNT, an n-gram method that scales language models on host memory
COUNT stores precomputed n-gram counts on host RAM instead of training learned embeddings, and reportedly matches a model with 57% more compute for 1B-parameter pretraining. The results come from an arXiv paper shared on Reddit and are not independently verified.