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Dynamo is a distributed inference orchestration platform designed for large language models. It functions as a system to coordinate prefill and decode phases across GPU nodes, utilizing a multi-backend runtime adapter to connect engines like vLLM and TensorRT-LLM through a unified block-oriented memory interface. An OpenAI-compatible API server provides the frontend for integration with existing tools and clients. The project is distinguished by its disaggregated serving architecture, which separates prompt processing and token generation onto independent GPU pools to optimize throughput and
FlexGen is an inference engine for large language models designed for high-throughput execution on single or multiple GPUs. It functions as a framework for managing model execution through a combination of memory offloading, weight compression, and pipeline orchestration. The system enables the execution of models that exceed available GPU memory by moving tensors and caches between GPU memory, system RAM, and disk storage. It utilizes 4-bit weight quantization to reduce the memory footprint of model parameters, allowing for increased batch processing capacity. The project covers distributed
Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr
This project is a long context inference engine and optimizer designed to process infinite text streams using large language models without memory growth or performance degradation. It serves as a system for maintaining constant memory usage during the generation of text from arbitrarily long input sequences.
The main features of mit-han-lab/streaming-llm are: Rolling KV Caches, Streaming Attention Kernels, Infinite Sequence Processing, Constant-Memory Deployments, LLM Performance Optimization Libraries, Long Context Processing, KV Cache Management, KV Cache Eviction.
Open-source alternatives to mit-han-lab/streaming-llm include: nvidia/isaac-gr00t. ai-dynamo/dynamo — Dynamo is a distributed inference orchestration platform designed for large language models. It functions as a system… fminference/flexgen — FlexGen is an inference engine for large language models designed for high-throughput execution on single or multiple… vllm-project/vllm — vLLM is a high-throughput inference engine designed for the efficient serving and execution of large language models.… sgl-project/sglang — Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It… cstankonrad/long_llama — Long Llama is a transformer-based language model and fine-tuning framework designed to process and maintain logical…
