By Janakiram MSV

Copyright thenewstack

Large language model (LLM) inferencing has evolved rapidly, driven by the need for low latency, high throughput and flexible deployment across heterogeneous hardware.

As a result, a diverse set of frameworks has emerged, each offering unique optimizations for scaling, performance and operational control.

From vLLM’s memory-efficient PagedAttention and continuous batching to Hugging Face TGI’s production-ready orchestration and NVIDIA Dynamo’s disaggregated serving architecture, the ecosystem now spans research-friendly platforms like SGLang, Kubernetes-native stacks like llm-d and enterprise-oriented control planes like AIBrix. This article explores these frameworks in detail, highlighting their design choices, technical innovations and suitability for diverse, real-world deployment scenarios.

vLLM: Optimized Inference With PagedAttention

vLLM is a highly optimized inference engine for LLMs, created at UC Berkeley and now developed by a global community. The framework is centered around the PagedAttention mechanism, which enables more efficient and granular management of the key-value cache memory needed for transformer attention. This gives vLLM an edge when serving workloads with long context windows and large batches. Another innovation, continuous batching, keeps the GPU busy by dynamically inserting and retiring requests from a batch. Additional features include guided decoding via grammar-constrained finite-state machines, chunked prefill for handling lengthy prompts, prefix cache sharing, speculative decoding and hardware support covering NVIDIA, AMD, Intel and even emerging accelerator platforms. The engine exposes an OpenAI-compatible API, integrates seamlessly with Hugging Face models and supports multi-GPU, multinode deployment with tensor, pipeline and expert parallelism. vLLM is often chosen for production environments where maximizing server throughput and latency is vital.

Hugging Face TGI: Enterprise-Ready Inference Serving

Hugging Face Text Generation Inference (TGI) is the favored serving platform for enterprises using Hugging Face’s model ecosystem. TGI is designed to efficiently scale LLM inference across many GPUs and nodes. Key features include smart batching, quantization support (INT4, INT8, FP8), GPU acceleration and orchestration for multiple models. TGI may run as either a standalone service or be integrated with cloud deployment tools for robust monitoring and autoscaling. Its API is compatible with both Hugging Face and production REST endpoints, with a rich toolkit for observability and logging. Recent releases have focused on efficient sharding across GPU clusters, autoregressive token scheduling and advanced quantization for minimizing latency and maximizing throughput. TGI is especially popular among organizations with diverse model requirements and high-volume, multitenant production chat or content generation workloads.

SGLang: Programmable Control for Complex LLM Workflows

SGLang brings together advanced speed with programmable control over LLM runtime flows. Designed for both researchers and production teams, SGLang offers a dedicated scripting language for chaining operations and managing model logic. RadixAttention is a core technology that enables cache reuse for sequences with similar prefixes — a significant advantage in agentic and multimodal applications. The backend runtime can leverage continuous batching, tensor and pipeline parallelism, speculative decoding and robust multimodel orchestration. SGLang excels at handling multistep reasoning tasks or integrating LLMs with other AI toolchains, including vision and retrieval models. Through its structured scripting frontend and runtime optimizations, SGLang gives developers fine-grained control for dynamic, complex LLM deployments.

NVIDIA Dynamo: Disaggregated Serving for Hyperscale Performance

NVIDIA Dynamo is a state-of-the-art distributed inference framework that leverages NVIDIA’s expertise in high-performance computing and generative AI (GenAI). Dynamo is architected around disaggregated serving, which divides the prefill and decode phases of LLM requests. This separation enables dynamic GPU assignment and much higher utilization when handling thousands of concurrent clients. Dynamo is written in a combination of Rust for orchestration and Python for extensibility, and can employ multiple backends, including vLLM, TensorRT-LLM and custom engines. Key technical advances in Dynamo are the NIXL library for accelerated interconnects, dynamic GPU-to-request routing, advanced cache offloading and modular plugin support. Dynamo is designed for environments where ultra-low latency, resilience and rapid scaling across data center clusters are priorities. It has gained traction in hyperscale providers and large enterprises seeking unmatched elasticity.

AIBrix: Cloud Native Orchestration and Control

AIBrix serves as an orchestration and control plane for cloud native, research-friendly LLM serving. Built natively for Kubernetes, AIBrix coordinates dynamic scheduling, model policy enforcement, autoscaling, LoRA management and plugin registration for open source backends like vLLM. Mix-grain orchestration allows inference requests to be spread across multinode clusters with hybrid scheduling using Kubernetes and Ray. Its distributed KV cache enables efficient memory use and high reliability, while advanced routing and service-level objective (SLO)-driven optimization ensure fast and fair serving even under intense load. The framework is also equipped to manage multimodal and multiadapter deployments, supporting fine-tuning, LoRA updates and cost-effective resource placement. Enterprises and research teams favor AIBrix for its policy-driven workflow, enterprise management features and full support for open APIs and adapters.

llm-d: Kubernetes-Native Distributed Serving

llm-d targets Kubernetes-native distributed LLM serving, building on deep integration with vLLM and providing research and production teams with scalable, observable inference stacks. It introduces an Inference Gateway that handles fast request routing, autoscaling and granular resource management. Disaggregated serving separates prefill and decode to lower latency, and pooled KV cache management improves memory footprint. The llm-d architecture emphasizes operational telemetry and transparent deployment, with support for high-availability patterns and OpenAI-compatible endpoints. As a cloud native platform, llm-d runs most efficiently on multi-GPU NVIDIA clusters managed under Kubernetes, simplifying the rollout and monitoring of LLM workloads for ongoing research, development and commercial deployments.

These frameworks reflect the evolution of LLM serving toward higher throughput, lower latency, programmable orchestration and seamless scaling. vLLM and Hugging Face TGI are widely adopted in production for their speed and ecosystem compatibility. SGLang is chosen for advanced agentic applications and multimodal tasks. NVIDIA Dynamo pushes the edge with data center-grade elasticity and backend flexibility. AIBrix delivers orchestration and policy control for enterprise and research workflows. llm-d combines Kubernetes-native scaling and robust observability for ongoing cloud experiments and production rollout.

As LLM workloads grow, these solutions continue to set new standards in performance, flexibility, and reliability.