1 link tagged with all of: llm inference + transformers + quantization
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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.