Multi-Query Batch Inference Optimization
Achieve 15.6x throughput improvement with continuous batching, priority scheduling, and CPU-optimized LLM inference
Problem Statement
We asked NEO to: Build a high-performance CPU-based LLM inference server for Mistral-7B that efficiently handles mixed workloads with continuous batching for throughput, priority-based scheduling for low latency on interactive requests, and grammar-constrained decoding for reliable structured JSON outputs.
Solution Overview
NEO built a production-ready inference optimization system delivering:
- 15.6x Throughput Improvement: Continuous batching vs sequential processing
- <500ms Interactive Latency: Priority-based scheduling with preemption
- 72% Memory Reduction: Block-based KV cache management
- 100% Valid JSON: Grammar-constrained decoding with minimal overhead
The system handles mixed interactive and batch workloads on commodity CPU hardware while maintaining efficient resource utilization.
Workflow / Pipeline
| Step | Description |
|---|---|
| 1. Request Ingestion | FastAPI server receives requests with priority flags (interactive vs batch) |
| 2. Priority Queueing | Requests sorted into priority queues with real-time preemption support |
| 3. Continuous Batching | Dynamic request join/leave mid-generation for optimal compute utilization |
| 4. Model Inference | Mistral-7B (GGUF quantized) generates tokens with 4-core CPU threading |
| 5. Memory Management | Block-based KV cache allocation with shared prefix caching (72% reduction) |
| 6. Output Processing | Raw text or grammar-constrained JSON generation with validation |
| 7. Response Delivery | Return generated text with detailed performance metrics |
Repository & Artifacts
Generated Artifacts:
- FastAPI inference server with async support
- Continuous batching engine implementation
- Priority-based scheduling system
- GBNF grammar-constrained decoder
- Block-based memory management module
- Performance benchmark suite
- Comprehensive metrics and monitoring
Technical Details
- Model: Mistral-7B-Instruct (GGUF quantized)
- Framework: llama-cpp-python with CPU optimization
- Server: FastAPI with async request handling
- Batching: Continuous batching with dynamic join/leave
- Scheduling: Two-tier priority queue (interactive/batch)
- Memory: PagedAttention with block-based KV cache
- Structured Output: GBNF grammar compilation from JSON schemas
- Threading: 4-core CPU parallelization for optimal throughput
- Performance: ~6 tokens/sec single request, 18.7 requests/sec batched
Results
- Throughput: 18.7 requests/sec (15.6x vs sequential baseline of 1.2 req/sec)
- Interactive Latency: <500ms (P95: 320ms, P99: 380ms)
- Batch Latency: P50: 450ms, P95: 850ms
- Memory Efficiency: 6.8 GB vs 24 GB traditional (72% reduction)
- Structured Output: 100% valid JSON with 4.61% overhead
- CPU Utilization: 94% at batch size 8 (optimal parallelization)
- Cache Hit Rate: 85.3% with shared prefix caching
Best Practices & Lessons Learned
- Continuous batching dramatically improves throughput on CPU by maximizing compute utilization
- Priority-based scheduling is essential for maintaining interactive responsiveness under load
- Block-based memory management reduces waste and enables larger effective batch sizes
- Grammar-constrained decoding is more efficient than post-processing with retry loops
- 4-core threading provides optimal balance between parallelization and overhead
- Shared prefix caching significantly reduces redundant computation for similar prompts
- GGUF quantization enables efficient CPU inference without major accuracy loss
Next Steps
- Add GPU support for hybrid CPU/GPU inference pipelines
- Implement speculative decoding with smaller draft models
- Create dynamic batching based on real-time server load
- Add LoRA adapter hot-swapping for per-request customization
- Build multi-model routing based on complexity detection
- Implement token streaming with WebSocket support
- Create Docker container with optimized CPU settings
- Add Prometheus metrics exporter for production monitoring