Models & Frontiers
What the new models can actually do, how they were trained, and whether the benchmarks mean anything. Open source vs closed, and where the research is heading.
Key Guides
The GPU Bottleneck Isn't Compute Anymore
NVIDIA's Blackwell GPUs doubled tensor core throughput but left shared memory and exponential units unchanged. FlashAttention-4 rearchitects attention kernels from scratch to work around this asymmetry, achieving 1,613 TFLOPs/s and up to 1.3x speedup over cuDNN on B200.
MoE Training Just Got 4x Faster
Grouter extracts routing structures from pre-trained MoE models and reuses them as fixed routers for new models. The result: 4.28x improvement in data utilization and up to 33.5% throughput acceleration.
LLM-Powered Swarms and the 300x Overhead Nobody Wants to Talk About
SwarmBench tested 13 LLMs on swarm coordination tasks. The results show catastrophic overhead and communication that doesn't actually help.
Attention Heads Are the New Inference Budget
Models that can technically process 128K tokens routinely fail on tasks requiring reasoning across 32K. That gap isn't a context window problem. It's an...
MoE's Dirty Secret Is Load Balancing
Every frontier lab now ships a sparse Mixture-of-Experts model. Google's Switch Transformer started the trend. DeepSeek-V3 proved it could scale....
Synthetic Data Won't Save You From Model Collapse
The AI industry's running out of internet. Every major lab's already scraped the same corpus, and the easy gains from scaling data are tapering. The...
MoE Models Run 405B Parameters at 13B Cost
When Mistral AI dropped Mixtral 8x7B in December 2023, claiming GPT-3.5-level performance at a fraction of the compute cost, the reaction split cleanly...
The Inference Budget Just Got Interesting
OpenAI's o1 made headlines for "thinking harder" during inference. But the real story isn't that a model can spend more tokens on reasoning: it's that...
Mixture of Experts Explained: The Architecture Behind Every Frontier Model
Every frontier model released in the last 18 months uses Mixture of Experts. DeepSeek-V3 activates just 37 billion of its 671 billion parameters per token. Understanding how MoE works isn't optional anymore.
Inference-Time Compute Is Escaping the LLM Bubble
Explore how inference-time compute scaling lets AI models think longer and reason deeper, boosting accuracy without retraining.