Serving large models is where research meets the bill. Quantization and low-bit formats, speculative decoding, KV-cache management, batching and throughput tradeoffs, and what long context actually costs in production.
DeepSeek DSpark adds semi-autoregressive drafting and load-aware verification to speculative decoding. What is new versus EAGLE-3, and why the benchmarks are not yet independently verified.
A hands-on speculative decoding tutorial for vLLM: how it works, runnable n-gram and draft-model examples on Qwen3, EAGLE-3, and where the speedup disappears.
A technical guide to LLM quantization: FP8 training, NVFP4 and MXFP4, W4A4 inference, the outlier problem, and where low-bit precision quietly breaks accuracy.
Master LLM inference optimization: speculative decoding, KV-cache compression, quantization, FlashAttention, and serving frameworks compared for fast, cost-effective AI.
Learn how Mixture of Experts (MoE) powers frontier AI models like DeepSeek-V3 and Mixtral: sparse routing, load balancing, and why MoE beat dense scaling.
Explore DeepSeek's architecture breakthroughs: Multi-Head Latent Attention, auxiliary-loss-free MoE, FP8 training, and GRPO: frontier AI for $5.5M.