Ai

The final "Black Tech" isn't a single line in the config—it’s the philosophy behind the whole thing. Look at the compress_ratios again. Look at the sliding_window: 128. Look at the rope_scaling: yarn. This is a model built by people who know

In the world of AI business, "Throughput is King." If your model is smart but can only talk to one person at a time, you’re going to go broke. DeepSeek V4 is designed to be the "Buffet King." Because of the num_nextn_predict_layers

If you told someone five years ago you were building a 1.5 Trillion parameter model, they’d ask if you owned a nuclear power plant. That’s because "Dense" models are power-hungry monsters. But DeepSeek V4 is a "Sparse" Ghost. With 384 routed experts and

Most people don't realize that when you "quantize" a model to make it run faster (like using FP8 or INT4), you usually lose some of the model's IQ. It’s like lobotomizing a genius so they can fit into a smaller room. But DeepSeek

The biggest cost in running an AI isn't the electricity to "think"—it’s the "memory" needed to hold the conversation. This is the KV Cache. For a 1.5T model, the KV Cache for a long conversation could easily swallow an entire cluster

In the MoE world, "Auxiliary Loss" is the "Training Wheels" of AI. You have to tell the model, "Hey, don't just use Expert #1, give Expert #2 some work too!" If you don't, the model becomes "lazy," and

Most LLMs are like that annoying friend who finishes your sentences, but they do it one word at a time. "I... want... to... eat... pizza." DeepSeek V4 is the friend who has already ordered the pizza, set the table, and predicted you'll want extra olives. That’

Every "AI Bro" on Twitter will tell you that the future is RAG (Retrieval-Augmented Generation). They say you need to plug your model into a Vector Database so it can "search" for facts. DeepSeek V4 looked at that and said, "Why don't we

Running a 1.5-Trillion parameter MoE (Mixture of Experts) is like trying to manage a kitchen with 384 Michelin-star chefs, but only 6 of them are cooking at any given time. Usually, this is a logistics nightmare. The "Router" (the head waiter) gets overwhelmed, chefs sit idle, and

Imagine Uncle Sam building a 100-story warehouse just to store the paperwork for a single project. That’s how Silicon Valley handles "Long Context." They throw H100s at the problem until the power grid screams. Then comes DeepSeek V4. Looking at the compress_ratios in the leaked config,