大模型推理优化：从 vLLM 到自研推理引擎的演进之路
LLM Inference Optimization: Evolution from vLLM to Our Custom Inference Engine
| iDev Engineering | 2026-08-27T11:11:16
iDev 工程团队分享大模型推理引擎的优化历程，从使用开源 vLLM 到自研高性能推理引擎 iServe，实现 3 倍吞吐量提升和 40% 成本降低。
iDev engineering team shares the optimization journey of LLM inference engines, from open-source vLLM to the custom high-performance inference engine iServe, achieving 3x throughput improvement and 40% cost reduction.
优化动机iDev 平台每天处理超过 1.2 亿次 AI 代码生成请求，推理成本占总运营成本的 45%。降低推理延迟和成本是我们面临的核心工程挑战。vLLM 阶段初期我们使用 vLLM 作为推理后端，其 PagedAttention 机制有效提升了 GPU 显存利用率。但在超大规模并发场景下，vLLM 的调度策略和内存管理存在瓶颈。自研 iServe 引擎核心优化点动态批处理：基于请求优先级和预估长度的智能批次组装前缀缓存：对高频代码模板的 KV Cache 进行持久化复用投机采样：使用 68M 小模型预测 draft tokens，大模型验证量化推理：INT8/FP8 混合精度，精度损失低于 0.3%调度算法# 简化版调度逻辑 def schedule_batch(pending_requests): sorted_reqs = sort_by_priority_and_length(pending_requests) batch = [] total_tokens = 0 for req in sorted_reqs: if total_tokens + req.est_tokens 性能对比在 A100 80G GPU 上，使用 CodeLlama-34B 模型的测试结果：iServe 相比 vLLM 吞吐量提升 3.2 倍，首 token 延迟从 180ms 降至 65ms，GPU 利用率从 72% 提升至 91%。未来方向团队正在探索跨节点推理、基于 RDMA 的 KV Cache 共享和自适应模型路由等技术。
Optimization MotivationThe iDev platform processes over 120 million AI code generation requests daily, with inference costs accounting for 45% of total operating costs. Reducing inference latency and costs is our core engineering challenge.vLLM PhaseInitially, we used vLLM as the inference backend. Its PagedAttention mechanism effectively improved GPU memory utilization. However, under extreme concurrency, vLLM's scheduling strategy and memory management had bottlenecks.Custom iServe EngineCore OptimizationsDynamic Batching: Intelligent batch assembly based on request priority and estimated lengthPrefix Caching: Persistent reuse of KV Cache for high-frequency code templatesSpeculative Sampling: Using a 68M small model to predict draft tokens, verified by the large modelQuantized Inference: INT8/FP8 mixed precision with less than 0.3% accuracy lossScheduling Algorithm# Simplified scheduling logic def schedule_batch(pending_requests): sorted_reqs = sort_by_priority_and_length(pending_requests) batch = [] total_tokens = 0 for req in sorted_reqs: if total_tokens + req.est_tokens Performance ComparisonOn an A100 80G GPU using the CodeLlama-34B model: iServe achieved 3.2x throughput improvement over vLLM, first token latency reduced from 180ms to 65ms, and GPU utilization increased from 72% to 91%.Future DirectionsThe team is exploring cross-node inference, RDMA-based KV Cache sharing, and adaptive model routing.