Analysis Compares Performance, Size, and Hardware for Open-Weight LLMs
An analysis comparing recent open-weight large language models shows that Meta's 816B Muse Spark Open scores 39 on Artificial Analysis, while the 180B Qwen3.8-Flash-Next achieves a slightly higher score of 40. The evaluation maps aggregate benchmark scores against total parameter sizes and local hardware requirements. This comparison provides crucial insights for local AI practitioners trying to balance benchmark performance against massive hardware constraints. Understanding the trade-offs between total parameters, active parameters, and quantization sizes helps users determine feasibility for local deployment. Unsloth GGUF quantizations require about 506 GB for Muse Spark Open and 111 GB for Qwen3.8-Flash-Next, with Meta recommending two 512 GB M5 Ultra Mac Studios for the former. Meanwhile, top models like MiMo-V2.6-Pro reached an index score of 46.
## BACKGROUND
Mixture of Experts models use a routing system to activate only a subset of parameters for each token, improving speed and cost efficiency relative to their massive total size. GGUF is a single-file binary format created by llama.cpp that packages model weights and metadata to facilitate efficient local inference.