Context Distillation & KV Compression

Saving VRAM at the Token Level

Context Distillation Flow

Every token in your context window costs GPU VRAM inside the KV Cache. For 100,000 active users with 10k-token histories, the memory requirement scales linearly to petabytes. Senior engineers actively compress context payloads before inference.

Semantic Distillation

The simplest approach: do you need the exact text of the Wikipedia article? No. You only need the facts. You run a cheap model (Llama-3-8B) offline to distill the 5,000-token article down to a 500-token dense factual summary. You embed and serve the summary. This 10x compression saves massive KV VRAM and speeds up inference calculation.

Prompt Compression (LLMLingua)

Instead of summarizing text, you can mathematically compress the prompt by removing tokens that Microsoft's LLMLingua scores as having low perplexity (meaning the LLM predicts them easily and doesn't need them to understand context). This removes stopwords, prepositions, and predictable grammar, reducing the prompt length by 40-50% while preserving the "semantic signal." The prompt looks broken to a human, but the LLM understands it perfectly.

Trade-off: Compression algorithms take compute time. You trade CPU/GPU cycles during compression to save TTFB latency and API costs on the LLM endpoint.