How to Make Clean, Maintainable Modifications to vLLM Using the Plugin System: A Practical Guide to Avoiding Forks and Monkey Patches
5 hours 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
6 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 …