The Compression Revolution You’ve trained a massive language model. It’s brilliant—answers complex questions, writes elegant code, reasons through multi-step problems. There’s just one problem: it requires eight GPUs to run inference and costs a…
Context windows keep getting bigger. GPT-4 Turbo opened with 128K. Gemini 1.5 Pro claimed 1M tokens. Some models advertise “infinite” context through various tricks. But bigger isn’t always better, and the numbers on the…
Every frontier model released in 2025 and 2026 uses some form of Mixture of Experts. Mixtral proved it works at medium scale. DeepSeek proved it works at massive scale. Grok proved it works for…
Standard attention is quadratic. Every token attends to every other token, making the computational cost grow with the square of the sequence length. At 128K tokens, that’s 16 billion attention computations per layer. The…
In 2022, DeepMind’s Chinchilla paper reshaped the AI industry. The claim: for compute-optimal training, scale parameters and data tokens equally. A 70B model needs ~1.4T tokens. The industry rearranged itself around this law. Then…
The cloud isn’t always an option. Sometimes latency requirements demand on-device inference. Sometimes privacy regulations prohibit sending data to external servers. Sometimes you’re building for environments with unreliable connectivity. Edge AI — running language…
Seven billion parameters has become the Goldilocks zone of language models. Large enough to be genuinely useful. Small enough to run on a laptop. Cheap enough to serve at scale. The 7B class has…
Model merging is one of the strangest breakthroughs in open-source AI. Take two fine-tuned models, average their weights in the right way, and get a model that combines both specialties. No additional training required….
Your inference engine determines everything about how your model serves requests. Speed, throughput, memory efficiency, hardware compatibility — it all flows from this choice. The three dominant options in 2026 are vLLM, Hugging Face’s…
Choosing where to host your open-source model is one of those decisions that seems simple until you actually try to make it. Together.ai, Fireworks.ai, and RunPod represent three fundamentally different approaches to inference hosting….