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 and must offload them to RAM or SSD. When running with offloaded parameters%2C the inference engine can process batches of hundreds or thousands of tokens at the same time as just one token%2C making it a natural fit for speculative decoding. We propose SpecExec (Speculative Execution)%2C a simple parallel decoding method that can generate up to 20 tokens per target model iteration for popular LLM families. It utilizes the high spikiness of the token probabilities distribution in modern LLMs and a high degree of alignment between model output probabilities. SpecExec takes the most probable tokens continuation from the draft model to build a cache tree for the target model%2C which then gets validated in a single pass. Using SpecExec%2C we demonstrate inference of 50B%2B parameter LLMs on consumer GPUs with RAM offloading at 4-6 tokens per second with 4-bit quantization or 2-3 tokens per second with 16-bit weights.?quality=80&w=800)
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