The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding.
Just tested out LLaMA 4, and it’s seriously impressive. 🧠🔥 Way more accurate, fluent, and nuanced than LLaMA 2. Meta really stepped up their game!
The responses feel more natural and less robotic, especially in longer chats. It’s fast, handles reasoning better, and can hold context like a pro. Definitely a strong rival to GPT-4 now.
This sounds like an exciting advancement in AI! The multimodal capabilities of the Llama 4 models could really enhance user experiences across various applications. I'm curious, how do you ensure the quality of image understanding alongside text processing? Looking forward to seeing how this technology evolves!
Congratulations on the release, the big model development is more perfect, aigc is coming!
Adapt to trend fast, Ghibli style
Llama 4, embedded in whatsapp powered by Meta, offers free of cost approximate all features like asking how to write message template and preparing for interview with self.
A strategic leap in AI scalability! The LLaMA 4 lineup—Scout, Maverick, and Behemoth—showcases Meta’s ambition to dominate both efficiency and performance. This tiered approach addresses diverse needs, from edge computing to enterprise-grade AI.
Love how open-source models are now beating the closed source ones. Curious if some new use cases will be opened up with 10M context length, previously even with 1M context length it's hard to direct the model what to do and usually accuracy drops.
🔥 That’s one wild new herd from Meta!
Llama 4 Scout sounds like the Swiss Army knife of small models—10M context lengthand runs on a single GPU? That’s huge for dev accessibility. Perfect for edge devices and lightweight agents.
Llama 4 Maverick might just be the sweet spot—beats GPT-4o and Gemini Flash 2, yet compact enough to run on a single host. Multi-modal, expert routing, and smaller than DeepSeek? That’s a massive win for efficient deployments.
And then there’s Llama 4 Behemoth—the name says it all. 2+ trillion parameters?! Sounds like Meta’s going head-to-head with Gemini 1.5 Pro and GPT-5-level ambition.
⚡️ This lineup shows Meta isn’t just playing catch-up anymore—they’re coming for every tier of the LLM stack:
Edge → Scout
Mid-range agents/apps → Maverick
Foundation model supremacy → Behemoth
Exciting to see how the mixture-of-experts approach is pushing performance in both text and image understanding.
Can't wait to try this out. We're experimenting with running models on-device for our product (desktop app) but haven't been able to get great results yet for the average laptop. Looking forward to see the reality of inference speeds for these models.
Impressive launch for Llama 4! Curious though—how do you manage efficiency and latency challenges with the mixture-of-experts setup, especially in real-time multimodal applications? @ashwinbmeta
