vllm and Automodel
These are complementary tools: vLLM provides optimized inference serving for already-trained models, while NeMo's Automodel handles distributed training and preparation of those models before deployment.
About vllm
vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
Implements PagedAttention for efficient KV cache management and continuous request batching to maximize GPU utilization. Supports multiple quantization schemes (GPTQ, AWQ, INT4/8, FP8), speculative decoding, and tensor/pipeline parallelism across NVIDIA, AMD, Intel, and TPU hardware. Provides OpenAI-compatible API endpoints and integrates directly with Hugging Face models, including multi-modal and mixture-of-expert architectures.
About Automodel
NVIDIA-NeMo/Automodel
Pytorch Distributed native training library for LLMs/VLMs with OOTB Hugging Face support
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