Laskamp4

: Designed for efficiency, this model has 17 billion active parameters. It fits on a single H100 GPU. It is optimized for high-speed performance (up to 460+ tokens per second) and long-document reasoning.

: Unlike previous versions that relied on "bolted-on" vision components, Llama 4 was trained from the start with text, images, and video frames. Laskamp4

: A defining feature is the 10 million token context window available in some variants, allowing the model to "read" over 7,500 pages of text or process 20+ hours of video in a single prompt. Key Models in the Series : Designed for efficiency, this model has 17

: The models use a "mixture of experts," where only a subset of the total parameters (e.g., 17 billion active parameters in the Scout model) are activated for any given task. This significantly reduces computational costs and latency while maintaining high performance. : Unlike previous versions that relied on "bolted-on"

The Llama 4 series represents a major shift in open-source artificial intelligence, moving toward capabilities and Mixture-of-Experts (MoE) architectures.

: This is a larger model with 400 billion parameters and 128 experts. It rivals top proprietary systems like GPT-4 and Gemini in complex reasoning, coding, and image understanding.

: Previews suggest this is Meta's most powerful model yet. It serves as a "teacher" for smaller models through distillation processes. Reception and Performance