Models & Labs
SpaceXAI and Cursor release Grok 4.5 model
The Rundown AIhigh confidence
Why it matters
- →Grok 4.5 offers significant efficiency gains, making it a cost-effective alternative to existing models.
- →The collaboration between SpaceXAI and Cursor highlights the potential for strategic partnerships in advancing AI capabilities.
- →The model's performance in coding and knowledge tasks positions it as a strong competitor in the AI landscape.
SpaceXAI and Cursor have unveiled Grok 4.5, a new AI model developed jointly after SpaceXAI's $60 billion acquisition of Cursor. The model demonstrates strong performance in coding and knowledge tasks, with benchmarks comparable to leading models like Claude Opus 4.8 and GPT 5.5. Grok 4.5 operates at a speed of 80 tokens per second and claims a fourfold efficiency improvement over its competitors. With pricing set at $2/$6 per million input/output tokens, it undercuts many frontier models, making it a competitive option in the AI market.
Read originalThe story around this
Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
More from The Rundown AI
More in Models & Labs
Models & Labsother
llama.cpp b12340 adds CUDA 13 and ROCm 10.0
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.
llama.cpp Releases
Models & Labscoding
llama.cpp b12342 adds few-row MMQ for speculative decoding
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.
