FastLLM is a high-performance inference engine for large language models designed for local execution and service deployment. The core runtime is implemented in C++ without PyTorch dependencies, supporting both dense models and mixture-of-experts architectures across CUDA, ROCm, CPU, NUMA, and disk-based inference with multi-card tensor parallelism capabilities.
The tool addresses the challenge of efficiently running large models on resource-constrained hardware by enabling flexible device placement and mixed-mode inference. Its approach distributes model layers across different compute resources—GPUs, CPUs, NUMA nodes, and storage—allowing users to run full-precision models on systems with limited VRAM but sufficient system memory or SSD capacity. The engine includes speculative decoding paths, multiple quantization formats, paged KV caching, and prefix caching to optimize throughput and latency.
Developers should choose this tool if they need to deploy large models locally with hardware constraints or require high-throughput inference services. It suits scenarios where a single GPU lacks sufficient memory for a model but the host system has ample CPU memory or storage. The project provides command-line interfaces, a web UI, performance benchmarking tools, and API services compatible with OpenAI Chat Completions and Anthropic Messages standards. The README emphasizes that operator support varies across different model architectures, quantization formats, and hardware backends, so validation with target hardware and models is necessary before production deployment.
Development activity shows consistent focus on expanding model coverage and optimization techniques. The project maintains active support for recent model families including Qwen, DeepSeek, Kimi, and GLM variants with specialized handling for their architectural features. Work spans multiple inference optimization strategies including speculative decoding variants, mixed-precision quantization paths, and attention mechanism optimizations. The codebase includes extensible backend infrastructure with built-in operators for multiple platforms and provisions for custom Python model graphs and third-party accelerator integration.
