1 link tagged with all of: llm inference + deepseek + transformers + quantization
Click any tag below to further narrow down your results
Links
This article breaks down how an LLM turns your prompt into streamed tokens, covering tokenization, embeddings, transformer attention, and the two-phase pipeline of compute-bound prefill and memory-bound decode. It explains KV caching, quantization, and metrics like Time to First Token and Inter-Token Latency to show why inference speed depends on both compute and memory.
- LLM inference splits into compute-bound prefill (TTFT) and memory-bound decode (ITL), which explains why the two phases have fundamentally different performance bottlenecks.
- KV caching avoids quadratic recomputation but costs real memory—about 1 MB per token for a 13B model, so a 4,000-token context burns 4 GB of VRAM.
- Techniques like INT8/INT4 cache quantization, token eviction, and paged cache management exist specifically to tame this KV cache memory growth.
- DeepSeek's V4 cuts KV cache size by 90% at a million-token context by combining sparse and dense compressed attention.