What it is

FastDeploy is a Python inference and deployment toolkit for large language models and vision-language models in the PaddlePaddle ecosystem. It serves models such as ERNIE, ERNIE-4.5, ERNIE-4.5-VL, DeepSeek V3, Qwen3-MoE, Qwen3-VL, and PaddleOCR-VL-0.9B on accelerators.

The concrete problem it addresses is operational complexity in high-throughput model serving. Teams need OpenAI-compatible endpoints, stable latency and throughput targets, heterogeneous accelerators, quantization, disaggregated prefill and decode, KV-cache movement, and speculative decoding without assembling those pieces separately.

Key capabilities

• Load-balanced prefill and decode disaggregation supports context caching and dynamic instance role switching to satisfy service-level objectives and improve resource utilization.
• A unified KV-cache transfer library supports intelligent selection between NVLink and RDMA for moving cache state across serving components.
• The serving layer exposes an OpenAI API and maintains compatibility with vLLM interfaces, allowing single-command deployment of online services.
• Quantization coverage includes W8A16, W8A8, W4A16, W4A8, W2A16, FP8, and the W4AFP8 method introduced in v2.5.
• Acceleration features include speculative decoding, multi-token prediction, chunked prefill, prefix caching, global cache pooling, and a load-balancing router.
• Hardware support spans NVIDIA GPUs, Kunlunxin XPUs, Hygon DCUs, Iluvatar GPUs, Enflame GCUs, MetaX GPUs, and Intel Gaudi accelerators.
• HuggingFace ecosystem model compatibility is listed among recent release capabilities, including support for ERNIE-4.5 and related model families.

Who uses it and how

• Engineering teams deploy production LLM and VLM services through online serving and quick-start guides, then connect clients through OpenAI-compatible endpoints.
• Platform operators running mixed accelerator clusters use the hardware-specific installation paths and serving features to standardize inference across NVIDIA and non-NVIDIA devices.
• Throughput-focused teams apply disaggregated deployment, KV-cache pooling, prefix caching, speculative decoding, and multi-token prediction to reduce serving cost for large models.
• Application teams that need batch or non-interactive processing use the offline inference workflow rather than a persistent API service.
• Model evaluators and integrators use the supported-model list and best-practice guides to run ERNIE, Qwen, DeepSeek, and PaddleOCR-VL workloads.

Getting started

FastDeploy requires Linux and Python 3.10 through 3.12, and its documentation directs users to hardware-specific installation guides for NVIDIA, Kunlunxin, Iluvatar, Enflame, Hygon, MetaX, and Intel Gaudi. Typical usage then follows the quick-start, online serving, and offline inference guides, with single-command deployment mentioned for OpenAI and vLLM-compatible services.

When to use it — and when not to

FastDeploy is most appropriate when the workload sits in the PaddlePaddle ecosystem, targets ERNIE or the listed accelerator families, and needs advanced serving features such as prefill-decode disaggregation, KV-cache transfer, and vLLM-compatible APIs. It is less attractive for projects that require non-Linux deployment, want a narrower NVIDIA-only vLLM stack, or cannot invest in hardware-specific installation and operational tuning. The 649 open issues and the absence of a contributor count indicate that teams should evaluate maintenance readiness before adopting it for critical production services.