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The Strange Economics of LLM Inference-as-a-Service
Fast-dLLM v2: Efficient Block-Diffusion LLM
How LLM Inference Actually Scales: KV Cache, Batching & vLLM
LLMs Don't Need More Parameters. They Need Loops.
Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding (M
Tinkering with DFlash2: How to Speed Up Local AI Models
10x Faster Than Standard LLM! DiffusionLM Explained
Mastering LLM Inference Optimization From Theory to Cost Effective Deployment: Mark Moyou
Small Language Models (SLMs) Are the Future: Fine-Tuning AI That Runs on Your iPhone
I Split LLM Inference Across Two GPUs: Prefill, Decode, and KV Cache
This LLM's Config Says 32,768 Tokens. It Reads 131,072. Here's the Trick.
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Last Updated: October 2, 2026
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Summary
I break down Sakana AI's DiffusionBlocks paper, then rebuild the mechanism myself on a mini model. Sign up for my FREE ... Why does DeepSeek's attention mechanism use 93% less GPU memory than standard Transformers? If you've ever wondered ... Attention mechanisms have been the key behind the recent AI boom. What happened after the multi-head attention in the seminal ... Try out Telnyx and use code BYCLOUD25 for $25 build credits! A deep dive into how looped language models change the scaling game. Our paper "Scaling Latent Reasoning via Looped ... Local models don't have to be slow — I explain DFlash- In this talk, I go over the rise of small language models (SLMs) and how they can benefit your business or day to day life. Kimi published a paper splitting Qwen2.5-7B-Instruct's own config.json says 32768 positions and rope_theta 1000000. Its model card says it reads 131072 tokens.
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