tiny-llm: Implementation and Optimization of a Lightweight LLM Inference Engine

tiny-llm is a lightweight inference engine designed to address LLM deployment challenges in resource-constrained environments (edge devices, embedded systems, low-cost servers). Implemented using CUDA C++17, it supports W8A16 quantized inference, KV cache management, and multiple sampling strategies. While maintaining acceptable performance, it significantly reduces resource consumption, providing an alternative for local deployment.

The resource requirements for LLM inference mainly come from model parameter storage and computation execution: a 70B parameter model requires about 140GB of VRAM for half-precision storage, which is a huge burden on consumer GPUs and edge devices. Demand scenarios include edge AI assistants, mobile offline translation, IoT intelligent interaction, etc. (limited hardware resources, latency tolerance, complete functionality). Traditional cloud APIs have issues like privacy risks, network dependency, and ongoing costs; tiny-llm provides an alternative for running optimized models locally.

tiny-llm is built with C++17 + CUDA, balancing performance and development efficiency. Its modular design includes a model loader (supporting multiple formats), computation kernels (hand-optimized Transformer operations), KV cache manager (pooled allocation, layout optimization, paged cache), and a pluggable sampler. W8A16 quantization: INT8 storage for weights + FP16 for activations, balancing size and precision, using CUDA dp4a instructions to optimize multiplication. KV cache management uses a pooling strategy to reduce allocation overhead, and paged cache supports long sequence processing.

Sampling strategies support greedy decoding, temperature sampling, Top-K, Top-P, and repetition penalty (combinable). Performance optimizations: Memory level (quantization halves usage, memory pool, weight sharing); Computation level (hand-tuned CUDA kernels, half-precision/Tensor Core, operator fusion); Batch processing level (dynamic batching to merge requests, continuous batching to keep the GPU busy).

Applicable scenarios: Edge devices (quantized models + NPU/GPU acceleration for interaction); Server-side (lightweight services for background tasks, multi-instance deployment); Research and education (clean code for learning LLM inference principles).

Comparison with llama.cpp: tiny-llm's advantages are modern C++ style and native CUDA support; llama.cpp's advantages are wide hardware support and mature ecosystem. Comparison with TensorRT-LLM: tiny-llm's advantages are lightweight and easy-to-modify code; TensorRT-LLM's advantages are extreme performance but high complexity and dependence on the NVIDIA ecosystem.

Plans include supporting more model architectures (state space models like Mamba, RWKV); expanding hardware support (AMD ROCm, Apple Metal); implementing more aggressive quantization (INT4, GPTQ); and adding speculative decoding to reduce latency.

tiny-llm enables LLM operation in limited resources through careful engineering. Its value lies in providing a usable inference engine with a clean design, serving as an excellent reference for edge deployment and inference learning. It is worth attention from those in resource-constrained scenarios and learners of inference principles.