Decoder Transformer Inference (LLM Serving)

inferencellm

Server Inference

• Transformer Inference Arithmetic | kipply’s blog

Prefill vs generation, compute bound vs memory bound

KV Cache

• [2412.19442] A Survey on Large Language Model Acceleration based on KV Cache Management

Grouped Query Attention

Less Keys and Values, to reduce Key Value Cache

Paged Attention

• Fast LLM Serving with vLLM and PagedAttention - YouTube

Avoid padding out samples with fixed size blocks, use proper paging, split up samples across smaller pages

Sliding Window Attention ⇒ Rolling Buffer KV Cache

• Exploring the Latency/Throughput & Cost Space for LLM Inference // Timothée Lacroix // CTO Mistral - YouTube
• SKVQ: Sliding-window Key and Value Cache Quantization for Large Language Models - YouTube

Cross Layer KV Cache Sharing

• [2405.12981] Reducing Transformer Key-Value Cache Size with Cross-Layer Attention

[2405.05254] You Only Cache Once: Decoder-Decoder Architectures for Language Models

• [2410.17897v3] Value Residual Learning For Alleviating Attention Concentration In Transformers

StreamingLLM

See Long Context Transformers

Prefix Caching / Prompt Caching

Cache KV cache for common prefixes

Radix Cache

Auto Prefix Caching

Cache Aware Request Routing

Low Precision and Compression

Use bf16, float8, int8 or smaller dtypes like int4 to save memory

• GitHub - NVIDIA/kvpress: LLM KV cache compression made easy

Ring Attention

Other

• [2410.03960] SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation

Speculative Decoding

• Dynamic Speculative Decoding
• How Speculative Decoding Boosts vLLM Performance by up to 2.8x | vLLM Blog
• [2403.09919] Recurrent Drafter for Fast Speculative Decoding in Large Language Models

Chunked Prefill (for batched inference)

Split up the prefill compute into chunks to reduce the units of work so that large prefills do not interfere with other request doing generation in the same batch

• [2403.02310] Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve

Disaggregated Prefill and Decoding

Do prefill on larger GPU then decode on smaller cheaper GPUs

• [2401.09670] DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving
• DistServe: disaggregating prefill and decoding for goodput-optimized LLM inference - YouTube

RadixAttention

Continuous Batching / Inflight Batching

• Accelerating LLM Inference with vLLM - YouTube

Ring Attention

Flash Attention

Flash Decoding

• Flash-Decoding for long-context inference | PyTorch

Mixture of Experts

Reduce compute, skipping experts

See Mixture of Experts

Multi LoRA Serving

Quantization

Structured Decoding / Generation

• Structured Generation with LLMs

• [2412.10418v1] Constrained Decoding with Speculative Lookaheads

Jump Forward Decoding for Known Structured Tokens

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LLM Routing

Predict best (speed / cost / expert) model for given request and route based on that

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Metrics

Time to First Token

Inter Token Latency

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Links

• Understanding the LLM Inference Workload - Mark Moyou, NVIDIA - YouTube
• Mastering LLM Techniques: Inference Optimization | NVIDIA Technical Blog
• Optimizing AI Inference at Character.AI
• [2402.16363] LLM Inference Unveiled: Survey and Roofline Model Insights
• [2407.12391] LLM Inference Serving: Survey of Recent Advances and Opportunities
• Optimizing AI Inference at Character.AI (Part Deux)
• Flash-Decoding for long-context inference | PyTorch