Optimizing Performance in Generative AI: Trimming Tokens
hackerone.com | blog | #ai-security | #llm | #performance | #generative-ai | #optimization | #tokens
Summary
Output tokens are generated sequentially and dominate LLM latency; trimming verbose inputs, capping output length, and using smaller models or parallelism can greatly improve generative AI performance.
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--- id: 1957 title: "Optimizing Performance in Generative AI: Trimming Tokens" source_domain: hackerone.com source_url: "https://www.hackerone.com/blog/optimizing-performance-generative-ai-trimming-tokens" published_at: "2024-01-22T09:57:49Z" tags: ["ai-security", "llm", "performance", "generative-ai", "optimization", "tokens"] phying_url: "https://phying.com/item/1957" lang: en --- # Optimizing Performance in Generative AI: Trimming Tokens ## Summary Output tokens are generated sequentially and dominate LLM latency; trimming verbose inputs, capping output length, and using smaller models or parallelism can greatly improve generative AI performance. ## Intelligence Metadata - **Sources:** hackerone.com - **Published:** 2024-01-22T09:57:49Z - **Original:** https://www.hackerone.com/blog/optimizing-performance-generative-ai-trimming-tokens - **Phying News:** https://phying.com/item/1957