
Thinking Machines Lab has launched Inkling, its first open-weight AI model, which allows developers to download and modify it directly. Unlike the proprietary models from OpenAI or Google, Inkling is designed for adaptability, using a mixture-of-experts system with 975 billion parameters. The model is aimed at enterprises looking to fine-tune AI for specific tasks, offering a customizable alternative to the one-size-fits-all approach. While not claiming to be the strongest, Inkling focuses on well-rounded performance and cost efficiency, challenging the dominance of closed AI models.
Read originalThe b10043 release of llama.cpp marks a notable enhancement with the addition of CUDA Virtual Devices, which significantly improves GPU resource management. By removing the NCCL path when virtual devices are in use, the update fine-tunes performance for these specific setups. This release also includes a comprehensive code refactor and the implementation of GPUx2 server CI jobs, reflecting a commitment to better testing and deployment processes. While there are no new model architectures, the update enhances the platform's flexibility across various operating systems, making it more adaptable for developers working with a wide range of hardware configurations.
The latest release of llama.cpp, b10051, addresses a critical issue in kernel dispatch by distinguishing between SME and SME2 capabilities. Previously, the integration treated SME as a single capability, leading to incorrect dispatch on SME(v1)-only hardware due to the use of SME2-specific instructions. This update introduces both build-time and runtime distinctions, ensuring that kernels are dispatched based on actual hardware support. This refinement enhances the accuracy and efficiency of operations on different hardware configurations, marking a significant improvement for developers working with these systems.