DualPath: How a New LLM Inference Architecture Breaks the Storage Bandwidth Bottleneck
4 hours ago 高效码农DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference A New Architecture That Boosts Multi-Turn AI System Performance Through Dual-Path KV-Cache Loading Introduction: When AI Agents Become Mainstream, Inference Architectures Face New Challenges Large Language Models (LLMs) are evolving from simple single-turn chatbots into intelligent agent systems capable of autonomous planning, tool invocation, and solving real-world tasks through multi-turn interactions. Whether it’s coding assistants or automated task agents, these applications all rely on multi-turn LLM inference—a long session process where context accumulates over time. This transformation brings a fundamental technical challenge: Agentic workloads become extremely I/O-intensive. Imagine an AI …
The Truth About LLM Workloads: Why One-Size-Fits-All APIs Are Costing You Performance and Money
5 days ago 高效码农The Truth About LLM Workloads: Why One-Size-Fits-All APIs Are Costing You We hold this truth to be self-evident: not all workloads are created equal. But for large language models, this truth is far from universally acknowledged. Most organizations building LLM applications get their AI from an API. These APIs hide the varied costs and engineering trade-offs of distinct workloads behind deceptively simple per-token pricing. However, the truth will out. The era of model API dominance is ending. This shift is thanks to excellent work on open source models by organizations like DeepSeek and Alibaba Qwen, which erode the benefits of …
How to Make Clean, Maintainable Modifications to vLLM Using the Plugin System: A Practical Guide to Avoiding Forks and Monkey Patches
3 months ago 高效码农In the field of Large Language Model (LLM) inference, vLLM has emerged as the preferred engine for developers and enterprises alike, thanks to its high throughput and low latency. It supports core features such as continuous batching, efficient scheduling, and paged attention, seamlessly handling deployments ranging from small-scale models to large frontier systems. However, as business use cases deepen, many teams face a common challenge: how to customize vLLM’s internal behavior without disrupting its original architecture. You might want to adjust scheduling logic, optimize KV-cache handling, or integrate proprietary optimization solutions—these needs may seem straightforward, but they often hide pitfalls. …
Unlocking 3x Faster LLM Inference on MacBooks: The KVSplit Quantization Breakthrough
9 months ago 高效码农Efficient LLM Inference on Apple Silicon: The KVSplit Breakthrough Introduction: Redefining Memory Constraints with Smart Quantization KV Cache Memory Comparison Running large language models (LLMs) on consumer MacBooks has long faced two critical challenges: memory limitations for long contexts and sluggish inference speeds. Traditional solutions forced trade-offs between precision and performance – until KVSplit introduced differentiated key-value quantization. This groundbreaking approach achieves: • 72% memory reduction • 3x longer context handling • 8% faster inference • <1% quality loss This deep dive explores the technical implementation, empirical results, and practical applications of this paradigm-shifting technology. Core Innovation: Why Treat Keys …