Enterprise AI Analysis
Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems
Authored by XUPENG MIAO (Purdue University), GABRIELE OLIARO (Carnegie Mellon University), ZHIHAO ZHANG (Carnegie Mellon University), XINHAO CHENG (Carnegie Mellon University), HONGYI JIN (Carnegie Mellon University), TIANQI CHEN (Carnegie Mellon University), ZHIHAO JIA (Carnegie Mellon University).
Executive Impact & Key Metrics
This survey provides a comprehensive understanding of the current state and future directions in efficient LLM serving, offering valuable insights for researchers and practitioners in overcoming the barriers of effective LLM deployment.
0
Increased TFLOPS (FP8)
0
Memory Reduction
0
Latency via Speculative Decoding
0
Scalability across diverse workloads
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Algorithmic Innovations
System Optimizations
Algorithmic Innovations for LLM Efficiency
Algorithmic modifications streamline the core generation process of LLMs, accelerating inference while carefully balancing speed and accuracy. Key areas include advanced decoding methods, novel architecture designs, and model compression techniques.
- Decoding Algorithms: Move beyond sequential token generation.
- Architecture Design: Optimize model structure for faster and resource-efficient inference.
- Model Compression: Reduce model size and computational footprint without significant performance loss.
Enterprise Process Flow: Auto-Regressive Decoding
Initialize input sequence with context/start token
→
Predict next token (argmax P(y|Xt-1))
→
Update input sequence (Xt = Xt-1 ⊕ yt)
→
Check for End-of-Sequence (EOS) token
→
Loop until EOS or max length reached
Speculative Decoding
Significantly increases parallelism without altering output, verified by original LLM.
System Optimizations for LLM Serving
System-level optimizations refine the underlying computational frameworks, maximizing hardware utilization and boosting performance. These techniques address memory, parallelization, scheduling, and low-level kernel efficiency.
- Low-bit Quantization: Reduce memory and computation by representing numerical values with fewer bits.
- Parallel Computation: Distribute workloads across multiple devices or nodes to enhance speed.
- Memory Management: Efficiently handle the large and dynamic memory demands of LLMs.
- Request Scheduling: Optimize the processing of incoming inference requests for better throughput and latency.
- Kernel Optimizations: Accelerate specific operations within the LLM inference pipeline by leveraging hardware features.
| Method | Key Characteristic | Benefits for LLM Serving |
|---|---|---|
| Selective Attention | Focuses on important tokens, discards uninformative context. |
|
| Sliding + Dilated Attention | Uses a fixed-size window with gaps for broader context. |
|
| Global Token Attention | Designates specific tokens for global context awareness. |
|
| Hash-based Attention | Uses hashing to group similar tokens, attending to representatives. |
|
Case Study: Deploying LLMs for Enterprise AI Assistants
A global enterprise, facing challenges in scaling its customer support and internal knowledge management, adopted advanced LLM serving strategies. By implementing low-bit quantization (INT8), they reduced the memory footprint of their custom LLMs by 40%, enabling deployment on less powerful edge devices. Utilizing paged attention and continuous batching, their serving infrastructure achieved a 3x increase in throughput for concurrent requests, significantly reducing customer wait times.
Furthermore, the integration of speculative decoding accelerated response generation by an average of 25% without compromising accuracy, crucial for real-time interactive AI assistants. These system and algorithmic optimizations resulted in a 30% reduction in operational costs and allowed the company to expand its AI assistant capabilities to millions of users, demonstrating the profound impact of efficient LLM serving.
Calculate Your Potential ROI
Estimate the operational savings and reclaimed human hours by optimizing LLM serving in your enterprise.
Estimated Annual Savings
$0
Reclaimed Annual Human Hours
0
Your AI Implementation Roadmap
A phased approach to integrate and optimize LLM serving for maximum efficiency and sustained impact.
Phase 1: Discovery & Strategy
Assess current LLM usage, identify bottlenecks, and define clear objectives. This phase involves a deep dive into your infrastructure, data pipelines, and specific application needs to tailor an optimization strategy.
Phase 2: Algorithmic & System Design
Design and implement specific algorithmic enhancements (e.g., speculative decoding, attention simplification) and system-level optimizations (e.g., paged attention, low-bit quantization, parallel computing) based on Phase 1 findings.
Phase 3: Integration & Testing
Integrate optimized LLM serving solutions into your existing enterprise systems. Rigorous testing for performance, accuracy, and stability across various workloads is conducted to ensure seamless deployment.
Phase 4: Deployment & Continuous Optimization
Roll out the optimized LLM serving infrastructure. Establish monitoring, feedback loops, and a framework for continuous improvement, including adaptive scheduling and ongoing kernel tuning to maintain peak efficiency.
Ready to Optimize Your LLM Serving?
Connect with our AI specialists to explore how these advanced techniques can transform your generative AI deployments.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat