FastLLM Deployment Framework — Implementation Deep Dive
Technical analysis of the FastLLM inference framework covering quantization, memory management, KV-cache optimization, and batched inference.
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FastLLM Deployment Framework
FastLLM is a C++ inference framework optimized for transformer-based LLMs on CPU and GPU hardware.
Architecture
Layered architecture: model loading, tensor computation, memory management. INT4 and INT8 quantization with per-group scaling factors reduces memory footprint by up to 4x while maintaining output quality.
Key Optimizations
Pre-allocated KV-cache buffers with page-based allocation avoid runtime allocation overhead. Continuous batching maximizes GPU utilization for serving workloads. Fused attention and feed-forward computations reduce kernel launch overhead.
Supported Models
LLaMA, ChatGLM, Baichuan, Qwen, and Mistral through a unified model loader.
Performance
RTX 4090: 60+ tokens/second for 7B models with INT4 quantization. CPU-only: 5-10 tokens/second via AVX-optimized operations.
APIs
C++ API for integration, HTTP server for remote inference, Python bindings for prototyping.
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