
Thinking Machines has launched Inkling, a new multimodal large language model available on Hugging Face. Inkling boasts nearly 1 trillion parameters and can handle text, image, and audio inputs simultaneously, making it a versatile tool for multimodal reasoning. The model uses a Mixture-of-Experts architecture, which allows for efficient inference by activating only a portion of its parameters at any given time. This release represents a significant advancement in AI, enabling developers to create more sophisticated applications that can understand and reason across different data types.
Read originalEarlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
© Hugging Face BlogIBM is tackling the brittleness of small language models by introducing LLMON, a structured markup language that explicitly separates instructions from data. By using reserved special tokens instead of ambiguous characters like JSON brackets, these new Granite 4.1 preview models drastically reduce indirect prompt injection attacks while maintaining standard performance. This approach forces the model to bind specific constraints to specific data payloads, eliminating the confusion that often leads to hallucinations or security breaches in long contexts.
This release quietly closes the hardware gap for local inference by adding default support for CUDA 13 and ROCm 10.0 across Linux and Windows. Users with newer NVIDIA GPUs or AMD accelerators no longer need to compile from source to get performance, while Apple Silicon builds now ship KleidiAI disabled by default, shifting the focus back to standard ARM optimizations. The inclusion of Snapdragon support for Linux and Android marks a significant step toward on-device AI beyond the x86/ARM desktop ecosystem. It is less about new features and more about ensuring llama.cpp remains the universal runtime for every available accelerator.
Speculative decoding just got a significant speed boost on NVIDIA GPUs. This release introduces a specialized kernel for 'few-row' matrix multiplications, targeting the small batch sizes typical of verification passes in speculative decoding workflows. By optimizing these specific operations for quantized formats like Q8_0 and IQ4 variants, llama.cpp reduces latency where it matters most during token generation. The change is CUDA-only for now but leverages hardware capabilities from Ampere onwards, making local inference faster without requiring new model architectures or complex configuration changes.
The Rundown AI · April 9, 2026 · Background
The Verge AI · May 11, 2026 · Related
TechCrunch AI · May 12, 2026 · Related
The Rundown AI · May 12, 2026 · Related
The AI Daily Brief · May 13, 2026 · Related
Matt Wolfe · May 16, 2026 · Related
Google DeepMind · June 10, 2026 · Background
OpenAI · June 24, 2026 · Background
vLLM Releases · July 27, 2026 · Related
MIT Technology Review AI · August 10, 2026 · Related
Lev Selector · August 12, 2026 · Related
Hugging Face Blog · September 24, 2026 · Related
Hugging Face Blog · October 27, 2026 · Related
Together AI launches Inkling model on day 0
5 developments
Hugging Face finally provides a concrete path to adapt the powerful TRELLIS.2 model for specific domains like footwear or furniture, solving the 'generalist' problem in 3D generation. By targeting only the structure and shape DiTs with LoRA adapters while freezing the texture flow, they prevent catastrophic forgetting of material priors—a common pitfall when fine-tuning large generative models. This approach allows practitioners to achieve high geometric accuracy on small datasets (188 samples) without retraining the entire pipeline. It transforms TRELLIS.2 from a demo toy into a usable tool for production-grade 3D asset creation.