ExLlamaV3 v1.0.0 - Major Performance Upgrades

After more than a year of development, ExLlamaV3 has released its first production version v1.0.0, bringing a suite of performance improvements and new features. The release removes flash‑attention‑2 and xformers dependencies, extending tensor‑parallel support to most models, including Gemma4. A new attention kernel with online cache quantization, dual input for SWA layers and attention sinks, eliminates slowdown for KV quantization and can even speed up inference. Graph paths are added for all attention and GDN modules, and a new conv1d kernel removes the need for causal_conv1d. Performance gains include greatly improved GEMM/GEMV on Ampere GPUs and a new INT8 GEMV kernel. The update also introduces a MoE kernel ticket scheduler, adds GptOssForCausalLM and NemotronHForCausalLM, and includes many minor optimizations, bugfixes, QoL improvements, and a faster extension build with more compilation units. Detailed performance metrics are shared in the release notes and a write‑up by the lead developer. The project is now ready for production workloads and community testing. The release is available on GitHub and the community can join the ExLlama Discord for support.