Enterprise AI Analysis: ODMA: On-Demand Memory Allocation Framework for LLM Serving on LPDDR-Class Accelerators
Optimizing LLM Inference on LPDDR-Class Accelerators: The ODMA Advantage
This analysis delves into ODMA, a novel on-demand memory allocation framework designed to overcome the limitations of current memory managers for serving Large Language Models (LLMs) on Random-Access-Constrained Device Memory (RACM) systems, such as LPDDR5-based accelerators. ODMA's approach addresses critical inefficiencies like static pre-allocation waste and the unsuitability of fine-granularity paging for LPDDR architectures, promising significant improvements in memory utilization and throughput.
Executive Impact: Key Performance Indicators
ODMA significantly enhances LLM serving efficiency on LPDDR-class accelerators by optimizing KV-cache memory allocation. It achieves this through a predictor-driven, hardware-aware approach, dynamic bucket partitioning, and a robust large-bucket safeguard mechanism. Our analysis reveals its capacity to boost resource utilization and request throughput, offering a superior solution compared to existing static or HBM-optimized methods.
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Improved Prediction Accuracy (Alpaca)
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RPS Improvement
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Memory Utilization (Alpaca)
Deep Analysis & Enterprise Applications
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Motivation & Problem
LLM serving on LPDDR5-class accelerators is hindered by current memory managers. Static pre-allocation wastes memory by reserving worst-case KV-cache, while fine-granularity paging (e.g., PagedAttention) is ill-suited for LPDDR's random-access constraints. ODMA aims to bridge this gap.
ODMA Architecture
ODMA combines a generation-length predictor, a dynamic bucket manager, and a large-bucket safety mechanism. It provides predictor-driven, hardware-conscious allocation to RACM accelerators, ensuring contiguous memory layout for streaming-friendly access.
Evaluation & Results
Evaluated on Alpaca and Google-NQ workloads using Cambricon MLU370-X4 accelerators, ODMA significantly improves length-prediction accuracy, device-memory utilization, and end-to-end throughput compared to static pre-allocation baselines.
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Improved Prediction Accuracy (Alpaca)
Enterprise Process Flow
Request Arrival
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Prediction & Tagging
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Task Pooling
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Scheduling
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Runtime Decoding
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KV-Cache Allocation
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Completion & Feedback
| Feature | Static Pre-allocation (Cambricon-vLLM) | PagedAttention (HBM-optimized) | ODMA (This Paper) |
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| Memory Utilization |
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| Random Access Tolerance |
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| Throughput (RPS) |
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| Adaptivity to Workload |
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Case Study: LLM Serving on Cambricon MLU370
ODMA was prototyped and evaluated on a node with four Cambricon MLU370-X4 accelerators, which feature LPDDR5 memory. This setup is representative of many production accelerators with RACM.
Challenge: Existing solutions like static pre-allocation waste significant device memory, leading to low utilization and throughput. PagedAttention, while effective on HBM, performs poorly on LPDDR due to random access penalties.
Solution: ODMA's predictor-driven dynamic bucket allocation, coupled with a large-bucket safeguard, provides just-in-time, just-enough KV-cache memory. This preserves streaming-friendly contiguous layouts while adapting to changing request length distributions.
Outcome: On Alpaca workloads, ODMA increased device-memory utilization from 55.05% to 72.45% and improved requests-per-second (RPS) by 29% and tokens-per-second (TPS) by 27% compared to a static pre-allocation baseline. This demonstrates efficient LLM serving on RACM platforms without hardware changes.
Calculate Your Potential AI ROI
See how ODMA's optimized memory allocation can translate into significant operational savings for your enterprise. Adjust the parameters below to estimate your return on investment.
Our Proven Implementation Roadmap
Our phased approach ensures a smooth, efficient, and tailored integration of ODMA into your existing LLM serving infrastructure on LPDDR-class accelerators.
Phase 1: Discovery & Assessment
Comprehensive analysis of your current LLM serving setup, workload patterns, and LPDDR hardware specifics to identify key optimization opportunities.
Phase 2: ODMA Integration & Customization
Seamless integration of the ODMA framework with your Cambricon-vLLM or similar runtime, including predictor training with your specific traces and dynamic bucket configuration.
Phase 3: Testing & Validation
Rigorous testing in a controlled environment, validating performance gains, memory utilization, and robustness under various workload conditions.
Phase 4: Production Deployment & Monitoring
Assisted rollout to production, continuous monitoring, and fine-tuning to ensure optimal performance and adapt to real-world distribution shifts.
Phase 5: Ongoing Optimization & Support
Post-deployment support, regular performance reviews, and updates to keep your LLM serving stack at peak efficiency.
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