/69a5a7f2064c85b44eb3710c323581ae/p161_1.jpg) ## Progress Points One progress point measures throughput. If I speed up ☐, how much faster do I run ☑?  Luke wants increase in ranking throughput ## What did Coz predict?  ranking 27% increase in ranking throughput Coz predicted a 21%

convolutions with FP16 inputs can run on Tensor Cores, which provide an 8X increase in computational throughput over FP32 arithmetic. APEX AMP is included to support models that currently rely on it, but torch convolutions with FP16 inputs can run on Tensor Cores, which provide an 8X increase in computational throughput over FP32 arithmetic. APEX AMP is included to support models that currently rely on it, but torch convolutions with FP16 inputs can run on Tensor Cores, which provide an 8X increase in computational throughput over FP32 arithmetic. APEX AMP is included to support models that currently rely on it, but torch

commitlog_sync_period_in_ms 72 commitlog_total_space_in_mb 72 compaction_preheat_key_cache 72 compaction_throughput_mb_per_sec 72 concurrent_compactors 72 concurrent_reads 72 concurrent_writes 72 flush_ reduce_cache_capacity_to 73 reduce_cache_sizes_at 73 sliced_buffer_size_in_kb 74 stream_throughput_outbound_megabits_per_sec 74 Remote Procedure Call Tuning Properties 74 request_scheduler 74 min_compaction_threshold 82 memtable_flush_after_mins 82 memtable_operations_in_millions 82 memtable_throughput_in_mb 83 rows_cached 83 row_cache_provider 83 row_cache_save_period_in_seconds 83 Java

make money, I have to have some kind of measurements, right?" Jonah talks him through it - Throughput - the rate at which the system generates money through sales - Inventory - all the money that inventory into throughput If the goal is to make money, then in terms of the measurements the goal is to reduce operational expense and reduce inventory while simultaneously increasing throughput. Is inventory When you lay off people, do you increase sales? Do you reduce your inventory? Parallel: What is throughput in terms of software? What is inventory? ## Two phenomena which are found in every plant Story:

easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management of attention key and value memory with PagedAttention - Continuous batching flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more - post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. - vLLM Meetups. ## DOCUMENTATION

easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management of attention key and value memory with PagedAttention - Continuous batching flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more - post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. - vLLM Meetups. ## DOCUMENTATION

easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management of attention key and value memory with PagedAttention - Continuous batching flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more - post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. - vLLM Meetups. ## DOCUMENTATION

easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management of attention key and value memory with PagedAttention - Continuous batching flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more - post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. ## DOCUMENTATION ## 1.1 Installation

easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management of attention key and value memory with PagedAttention - Continuous batching flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more - post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. ## DOCUMENTATION ## 1.1 Installation

easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management of attention key and value memory with PagedAttention - Continuous batching flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more - post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. - vLLM Meetups. ## DOCUMENTATION