The vLLM 0.21.0 release brings five major updates—including Transformers v4 deprecation, a C++20 build requirement, KV offload with hybrid memory, speculative decoding that respects thinking budgets, and a Blackwell token‑speed backend—while offering detailed upgrade guidance for different user groups.
vLLM tops the Artificial Analysis ranking by delivering the highest throughput for DeepSeek V3.2, Qwen 3.5 397B, and MiniMax‑M2.5 on identical NVIDIA Blackwell Ultra hardware, thanks to extensive kernel‑fusion optimizations that remain in the main branch.
This article compares six popular LLM inference frameworks—vLLM, TensorRT‑LLM, llama.cpp, ds4.c, Ollama, and Omlx—across performance, ease of use, and hardware compatibility, then provides a practical matrix to help users select the best fit for their GPU.
DFlash replaces the speculative draft model with a block‑diffusion drafter, generating 16 tokens per forward pass and achieving up to 6× speedup over baseline (2.5× over EAGLE‑3) without quality loss, while supporting a wide range of open‑source LLMs and multiple back‑ends.
TokenSpeed, an open‑source LLM inference engine designed for agent workloads, delivers TensorRT‑LLM‑level performance and vLLM‑level ease of use, outperforms TensorRT‑LLM by up to 11% throughput and halves latency on speculative decoding, and has earned Nvidia’s public recommendation.
This article introduces two recent GitHub projects—club‑3090, which enables single‑ or dual‑RTX 3090 inference of 27‑billion‑parameter models with detailed performance benchmarks, and library‑skills, a tool that keeps AI agents synchronized with the latest official library APIs—explaining their configurations, usage steps, hardware requirements, and target audiences.
Google’s new Multi‑Token Prediction (MTP) drafter for Gemma 4 delivers up to three‑fold inference speedups across hardware and frameworks—validated by official benchmarks and independent DGX Spark tests—while preserving identical output quality, and is immediately usable via Hugging Face, vLLM, MLX, Ollama and edge‑device runtimes.
The vLLM 0.20.1 patch, released shortly after 0.20.0, consolidates stability fixes and performance optimizations for DeepSeek V4, adds several bug fixes, updates installation instructions, and provides targeted upgrade recommendations for different user scenarios.
NVIDIA’s Nemotron 3 Nano Omni 30B‑A3B‑Reasoning model, an open‑source multimodal LLM with 30 B parameters, 256K context and video‑audio‑image‑text capabilities, outperforms comparable models by up to 9.2× in video throughput, runs on consumer GPUs via 4‑bit GGUF quantization, but currently supports only English input.
The vLLM 0.20.0 release dramatically upgrades the inference engine with DeepSeek V4 support, default CUDA 13, PyTorch 2.11, Transformers v5 compatibility, FlashAttention 4 MLA prefill, TurboQuant 2‑bit KV cache, an online quantization front‑end, IR enhancements, Model Runner V2 features, and a slew of new models, while providing detailed installation and upgrade guidance.
This article analyzes why vLLM's PagedAttention can cause GPU memory fragmentation and out‑of‑memory errors in production, presents four typical OOM scenarios, and provides concrete diagnostics, configuration tweaks, code examples, and monitoring strategies to eliminate the problem.
The article dissects DeepSeek‑V4’s local deployment using vLLM, explaining the steep hardware requirements, the complex heterogeneous KV‑cache architecture, and the aggressive kernel‑fusion and multi‑stream optimizations that together make high‑context inference both memory‑intensive and engineering‑heavy.
This guide details how enterprises can move large language models from lab to production by defining specific SLI/SLO metrics, diagnosing hidden bottlenecks such as tokenizer latency, and applying four quantifiable optimization levers that dramatically improve latency, throughput, and cost efficiency.
Cambricon leveraged its NeuWare stack and vLLM framework to deliver Day‑0 native support for DeepSeek‑V4‑flash (285 B) and DeepSeek‑V4‑pro (1.6 T), open‑sourcing the adaptation and showcasing rapid model migration alongside extreme performance optimizations across software and hardware layers.
The newly released Qwen3.6-27B dense multimodal model, at just 27 B parameters, surpasses the 397 B flagship on most encoding benchmarks, offers up to 1 M token context, supports FP8 quantization, and can be deployed locally via vLLM, SGLang or Transformers with modest hardware.
This article evaluates the Red Hat‑produced NVFP4‑quantized Qwen3.6‑35B model deployed with vLLM inside Docker on a dual‑RTX 4090 server, presenting accuracy gains, memory usage, initialization times, GPU compatibility notes, and practical deployment recommendations.
Kimi K2.6, an open-source 1-trillion-parameter MoE model, expands Agent capabilities with 256K context, multimodal inputs, and the ability to coordinate 300 sub-Agents over 4,000 steps, achieving top scores on benchmarks like Terminal-Bench 2.0, SWE-Bench Pro, and BrowseComp, while offering flexible deployment via vLLM, SGLang, and KTransformers.
The article details how to deploy the 4‑bit quantized Qwen3.6-35B model with vLLM 0.17 (and 0.19.1 patch) on a Docker container, compares its memory usage and token‑generation speed to Qwen3.5‑35B, and shares practical scripts and observed performance of roughly 150 tokens per second.
The article reviews three optimization paths for the Qwen3.6‑35B model—four‑bit AWQ quantization variants, the DFlash speculative decoding accelerator, and a Claude Opus‑based distillation—detailing their implementation steps, benchmark results, and guidance on selecting the best version for different hardware and performance needs.
The article provides a 2026 deep comparative analysis of three major large‑model inference frameworks—vLLM, llama.cpp, and MLX—detailing their core designs, recent updates, benchmark results on various hardware, deployment complexity, and recommended use cases to help developers choose the right tool.
The DFlash approach replaces speculative decoding’s autoregressive drafter with a block diffusion model and injects target‑model hidden features into every KV‑cache layer, achieving up to 5× speed‑up for Qwen3.5‑27B on single‑GPU and 1.5–1.9× on high‑concurrency workloads while preserving output quality.
This guide walks you through deploying Alibaba’s open‑source Qwen‑3 8B model on the SumW platform using vLLM, covering environment activation, server launch with OpenAI‑compatible parameters, SSH tunneling for remote access, and Python client calls, while highlighting key configuration tips and common pitfalls.
MiniMax‑M2.7, the newly open‑sourced 230‑billion‑parameter MoE model, offers self‑evolution, professional software engineering and agent capabilities, and can be deployed locally using Ollama, vLLM, SGLang or Docker with 4‑8 H200 GPUs, while the article details hardware needs, performance gains and tool‑calling/Thinking features.
The open‑source CoPaw‑Flash‑9B‑DataAnalyst‑LoRA model, fine‑tuned via LoRA, can autonomously load, explore, statistically analyze, visualize, and generate structured reports for CSV/Excel/JSON datasets, achieving a 90% success rate with an average of 26 iteration rounds, and it runs on a single consumer‑grade GPU using vLLM and the Data Analyst framework.
This article walks engineers through the full LLM stack—from tokenization and positional encoding to transformer blocks, efficient fine‑tuning, quantization, and production‑grade inference techniques such as KV‑cache, FlashAttention, PagedAttention, continuous batching, and speculative decoding—highlighting trade‑offs, toolchains, and practical workflow steps.
The open‑source GLM‑5.1 model, adapted to Baidu Baige's Kunlun XPU via the vLLM‑Kunlun Plugin, delivers record‑breaking SWE‑bench scores, eight‑hour autonomous coding, long‑context handling up to 64K tokens, and scalable deployment across tens of thousands of chips, showcasing end‑to‑end AI acceleration.
The vLLM 0.19.0 release adds first‑day Gemma 4 support, merges zero‑bubble asynchronous scheduling with speculative decoding, matures Model Runner V2, introduces full‑CUDA‑graph acceleration for ViT, generalizes DBO, brings CPU KV cache offload, and expands hardware and Transformers compatibility, offering substantial performance and flexibility gains for production LLM inference.
The article details how speculative decoding—using a fast small model to draft tokens and a large model to verify them—overcomes the memory‑bandwidth bottleneck of autoregressive inference, introduces SSD’s self‑draft and tree‑verification stages, presents real‑world benchmark gains, and shows how to enable it in vLLM.
The article reviews how the leading LLM inference frameworks—oMLX, mlx‑vlm, llama.cpp, and vLLM—are integrating Google’s TurboQuant compression, showing up to 79% KV‑cache memory reduction, near‑full‑precision decoding speed, and detailed integration steps for each project.
The March 2026 vLLM release bundle introduces four substantial upgrades—Semantic Router v0.2 Athena, NVIDIA Nemotron 3 Super, the parallel speculative decoding P‑EAGLE, and a completely re‑architected Model Runner V2—each backed by concrete benchmarks, architectural diagrams, and code examples that demonstrate how the engine evolves from a pure inference engine to a full‑stack AI serving platform.
The article explains how Prompt Caching leverages vLLM's PagedAttention and block‑level hashing to reuse KV cache across identical prefixes, dramatically cutting LLM inference latency and cost, and provides concrete engineering tips for maximizing cache hit rates.
Google’s TurboQuant reduces KV‑Cache memory by up to 4.6×, speeds 3‑bit attention computation up to 8× on H100, and delivers near‑zero accuracy loss across long‑context benchmarks, with open‑source implementations for Metal, vLLM and llama.cpp.
The article walks through constructing AI‑native infrastructure for agents, covering local inference deployment with vLLM, setting up an AI gateway using LiteLLM, implementing observability with logs, metrics, and tracing, and applying cost‑saving strategies that reduced latency, improved stability, and cut expenses by up to 60%.
This article dissects Step‑Audio2, an industrial‑grade multimodal large language model that unifies speech understanding, translation, dialogue and audio generation in a single causal LM, detailing its inference pipeline, key implementation tricks, deployment modes, strengths, limitations, and suitable application scenarios.
This article details the technical challenges of adapting the open‑source vLLM inference framework to Baidu's Kunlun XPU, outlines four major performance bottlenecks, and presents a multi‑dimensional optimization roadmap—including custom plugins, operator fusion, INT8 quantization, and CUDA‑Graph techniques—that together boost throughput by up to 8% and narrow the gap with leading GPU hardware.
This article walks through the complete workflow of fine‑tuning a Qwen2.5‑0.5B model with LLaMAFactory, covering environment setup, model download, dataset preparation, configuration editing, training execution, LoRA weight merging, and deployment via vLLM, while highlighting the framework’s minimal‑code and broad model support.
This article details the vLLM‑Kunlun open‑source project that adapts the high‑performance vLLM inference engine to Baidu's Kunlun XPU, covering platform overview, model‑porting workflow, plugin architecture, concrete case studies with MIMO‑Flash‑V2 and Qwen 3.5, and the performance‑tuning techniques that enable seamless, GPU‑level inference on domestic hardware.
This article presents a comprehensive, step‑by‑step methodology for troubleshooting and optimizing large‑language‑model inference performance, covering GPU, CPU, memory, network, configuration, and application layers, with concrete benchmark scripts, diagnostic commands, and real‑world case studies.
This guide walks through deploying a production‑grade vLLM inference service on Kubernetes, covering GPU resource scheduling, Service and Ingress configuration, session affinity, health checks, performance tuning, scaling, monitoring, fault‑tolerance, and best‑practice recommendations for high‑availability AI workloads.
The article walks through upgrading vLLM to 0.17.0, configuring Docker containers for 4090 GPUs, comparing FP8 and 4‑bit quantization of Qwen3.5 35B and 27B models, and presents detailed performance numbers and script parameters that reveal trade‑offs in memory usage and throughput.
The vLLM 0.17.0 release brings FlashAttention 4 integration, a mature Model Runner V2, complete Qwen 3.5 series support, a one‑click performance‑mode flag, Anthropic API compatibility, advanced weight‑offloading, broader hardware support beyond NVIDIA, ASR model integration, and detailed upgrade and installation guidance.
The article explains LLM inference stages, how KV‑cache and vLLM's Paged Attention enable cross‑request prompt caching, and shares practical guidelines for prompt ordering, immutable caching, and robust tool design that together shape efficient and reliable AI agent architectures.
This guide shows step‑by‑step how to enable or disable the thinking mode of Qwen3.5 series large language models across Ollama, LM Studio (GGUF and MLX), llama.cpp, and vLLM/SGLang using command‑line flags, custom model YAML files, and API parameters.
LMCache separates the KV cache from a vLLM instance into a shared service, dramatically cutting first‑token latency for repeated text, enabling multiple GPU instances to reuse cached vectors, improving hardware utilization, and supporting use cases such as long‑document QA, multi‑GPU load balancing, and prompt‑engineering, with a quick Docker‑based demo.
The article traces five eras of KV cache management for LLM inference—from its absence before Transformers to the emerging unified hybrid memory architecture—comparing vLLM, SGLang, and TensorRT‑LLM and offering a decision framework for selecting the right solution in various deployment scenarios.
This guide explains how to deploy vLLM for large‑language‑model serving on Kubernetes, covering GPU resource management, tensor‑parallel configuration, continuous batching, quantization choices, autoscaling with HPA and KEDA, multi‑model routing, and best‑practice recommendations for performance, cost control, and high availability.
This article reviews the three open‑source Qwen 3.5 models—including a 35B MoE, a 122B MoE, and a 27B dense version—detailing their parameter layouts, core attention designs, context length, inference performance, hardware requirements, and provides step‑by‑step code examples for loading them with Hugging Face Transformers and vLLM.
Alibaba’s Qwen3.5-397B-A17B, a 397‑billion‑parameter open‑source multimodal LLM, combines mixed linear attention with a sparse MoE architecture to achieve 8.6‑19× higher decoding throughput than Qwen3‑Max, supports 201 languages, and can be deployed via vLLM, Docker, Transformers, or SGLang with various optimization presets.
Qwen 3.5, an open‑source 397B‑parameter model that activates only 17B parameters, uses a hybrid MoE‑Gated Delta architecture, offers native multimodal support and a default chain‑of‑thought mode, and achieves benchmark scores comparable to GPT‑5.2, Claude 4.5 Opus and Gemini 3 Pro across code, math, agent and vision tasks.
This article explains how Baidu's new GLM-5 large model is adapted to the Kunlun P800 XPU, detailing the async reinforcement learning framework Slime, optimization techniques like INT8 quantization and tensor‑parallelism, and provides step‑by‑step deployment commands using the open‑source vLLM‑Kunlun plugin.
GLM-5, the new 744‑billion‑parameter open‑source LLM, expands on GLM‑4.5 with GlmMoeDsa architecture, achieves higher HLE benchmark scores than Claude Opus 4.5, demonstrates strong long‑context and agent capabilities, supports vLLM/SGLang, runs on various Chinese chips, and can directly generate Office documents.
The Tencent WeChat AI team introduces WeDLM, a diffusion language model that, through topological reordering, surpasses autoregressive models on the industrial‑grade vLLM engine with over threefold speedup on math reasoning and up to tenfold in low‑entropy scenarios, and provides a step‑by‑step online tutorial with GPU compute credits.
Step‑3.5‑Flash, a 196‑billion‑parameter open‑source LLM that activates only 11 B per token via a Mixture‑of‑Experts design, delivers 3‑plus‑times faster inference, matches top‑tier closed‑source models on SWE‑bench and other benchmarks, supports 256 K context, runs on consumer‑grade hardware, and is already integrated into vLLM, SGLang, and Claude Code, though it has known token‑efficiency and domain‑stability limitations.
GLM-OCR, a 0.9B‑parameter multimodal OCR model from Zhipu, achieves the highest score (94.62) on OmniDocBench V1.5, offers lightweight deployment via vLLM, Ollama, API and SDK, and outperforms larger rivals like DeepSeek‑OCR and PaddleOCR in speed and accuracy.
This article reviews practical techniques for accelerating large language model inference—including reduced‑precision formats, post‑training quantization, adapter‑based fine‑tuning, pruning, continuous batch processing, and multi‑GPU deployment—while providing concrete code examples, benchmark results, and guidance on selecting the right approach for production workloads.
Microsoft’s newly open‑sourced VibeVoice‑ASR model can transcribe up to 60‑minute audio in a single pass, preserving global context while providing built‑in speaker diarization and timestamps, supports 50+ languages, offers custom hot‑word injection, and can be deployed via Docker, Gradio, or vLLM for high‑throughput API services.
The Qwen3‑ASR series, now open‑sourced by Alibaba, offers three models (1.7B, 0.6B, and a 0.6B forced aligner) that cover 52 languages and 22 Chinese dialects, support streaming and offline inference, achieve an RTF of 0.064 with 2000× realtime throughput, handle singing with background music, and provide detailed deployment guides, benchmarks, and comparisons with other ASR solutions.
This guide walks through downloading the AWQ‑4bit quantized GLM‑4.7‑Flash model, upgrading vLLM, building a custom Docker image, and launching the model on two RTX 4090 GPUs with tuned parameters to avoid OOM, while sharing practical tips and observed performance.
The article details a step‑by‑step local deployment of DeepSeek‑OCR‑2, covering GPU memory requirements, accuracy on complex tables, long inference times, dependency hurdles like GCC, GLIBC and flash‑attn, and provides concrete solutions using conda environments and symlinks.
Kimi K2.5, Moonshot’s latest open‑source multimodal model trained on 15 trillion image‑text tokens, adds native vision capabilities and a 100‑agent swarm that speeds complex tasks by 4.5×, achieves top‑tier benchmark scores, and can be deployed with vLLM, while demanding significant resources and hardware.
This guide walks through setting up a Kunlun P800 XPU host, preparing Docker containers, deploying Qwen3‑8B/‑32B/‑VL models with vLLM‑Kunlun, benchmarking performance, and running full‑parameter DPO training using LLaMA‑Factory, providing scripts, configuration files, and troubleshooting tips for AI engineers.
DeepSeek-OCR 2 introduces Visual Causal Flow and a LLM‑based visual encoder, achieving 91.09% accuracy on OmniDocBench v1.5, while providing detailed installation, two inference modes (vLLM and Transformers), and an analysis of its strengths and limitations for complex document processing.
This article explains the NVFP4 4‑bit floating‑point quantization technique, shows how to deploy Qwen3‑30B‑A3B models with TensorRT‑LLM and vLLM, compares performance across NVFP4, AWQ and INT8 quantizations, and provides practical profiling commands for NVIDIA DGX systems.
Qwen3‑TTS, an open‑source text‑to‑speech model from Alibaba, offers sub‑100 ms first‑packet latency, supports voice cloning, natural‑language voice design, and ten languages, can be deployed locally on a GPU with as little as 8 GB VRAM, and integrates with ComfyUI for visual workflow building.
On isolated internal networks where the system GCC is stuck at version 4.8.5, the article shows how to bypass system upgrades by using Conda to install GCC 9 in a virtual environment, create symlinks, and compile AI projects like vLLM without root access.
GLM‑ASR‑Nano‑2512, a 1.5 B‑parameter open‑source speech‑recognition model released in December 2025, delivers state‑of‑the‑art accuracy on Chinese dialects and low‑volume audio, outperforms Whisper V3 on benchmark tests, runs on consumer GPUs, and provides detailed installation and deployment guides for transformers, vLLM and SGLang.
The vLLM core maintainers have founded Inferact, raised a $150 million seed round led by Andreessen Horowitz and Lightspeed, and highlighted escalating inference challenges, the project's ecosystem dominance, and a continued commitment to open‑source development.
SGLang, the open‑source inference accelerator, has been spun out into RadixArk—a $400 million‑valued startup aiming to democratize AI infrastructure, while the broader market sees a surge of funding for inference‑focused companies.
This tutorial explains the OpenAI‑style request and response schema, demonstrates low‑level API calls with the requests library, compares them to the high‑level openai package, and walks through building a streaming multi‑turn chatbot that interacts with a locally hosted large language model.
This guide details how to replace native Transformers inference with the high‑performance vLLM engine, leveraging PagedAttention, continuous batching, tensor parallelism, and OpenAI‑compatible APIs to achieve 3‑4× higher throughput, lower latency, and scalable multi‑GPU deployments for production‑grade large language models.
This article details how Baidu Cloud's hybrid‑cloud team leveraged the vLLM framework to cut the cold‑start time of massive models like Qwen3‑235B‑A22B from minutes to a few seconds through accelerated weight loading, CUDA‑graph capture postponement, cross‑instance state reuse, fork‑based process startup, and guard‑instance pre‑warming techniques.
Within two days, Baidu’s Baige and Kunlun Chip teams adapted the 309‑billion‑parameter MiMo Flash V2 model—featuring a hybrid SWA+Sink and Full Attention mechanism—to run efficiently on the Kunlun P800 XPU using the vLLM‑Kunlun Plugin, achieving lossless performance comparable to GPU inference.
In just two days, Baidu Baige and Kunlun's engineers extended the vLLM‑Kunlun Plugin to overcome asymmetric KV dimensions and integrate SWA+Sink attention, achieving lossless, high‑performance inference of the MiMo Flash V2 model on the Kunlun P800 XPU.
This article provides a step‑by‑step technical walkthrough of vLLM 0.8.4 on a single GPU, detailing the platform’s startup, model loading, Multi‑LoRA deployment, internal ZMQ communication, request scheduling, and inference execution, while exposing key source‑code snippets and architectural diagrams.
This guide walks through the complete process of deploying a high‑throughput large language model inference service using vLLM, covering environment preparation, installation, configuration tuning, performance testing, real‑world case studies, monitoring, troubleshooting, and backup strategies for production‑grade deployments.
This article examines why vLLM experiences out‑of‑memory errors in production, explains memory fragmentation caused by PagedAttention, outlines four typical OOM scenarios with concrete command‑line solutions, and provides deep analysis, configuration scripts, dynamic tuning, troubleshooting flowcharts, monitoring alerts, and best‑practice recommendations.
This article explains how Paged Attention, introduced by the vLLM team, applies virtual‑memory techniques, non‑contiguous block mapping, copy‑on‑write reuse, distributed scheduling, and hardware‑level optimizations to improve KV‑cache efficiency and reduce memory fragmentation in large language model serving.
This article explains why KV‑Cache hit rate is critical for large‑model inference, describes vLLM's automatic prefix caching, outlines the distributed cache challenges, and provides a step‑by‑step guide to deploying Alibaba Cloud ACK Gateway with Inference Extension's precise‑mode prefix‑cache‑aware routing, backed by benchmark results.
After discovering that only a few vLLM settings truly impact performance, this guide details how adjusting gpu_memory_utilization, max_num_batched_tokens, and enabling chunked prefill can raise Qwen2.5‑72B‑Instruct throughput from ~1800 to over 2500 tokens/s, improve latency, and provides comprehensive deployment, monitoring, and troubleshooting instructions.
The vLLM‑Kunlun Plugin, jointly released by Baidu Baige and Kunlun Chip, provides a high‑performance, zero‑intrusion solution for deploying open‑source large language models on domestic Kunlun XPU hardware, includes fused operators, precision‑validation and profiling tools, and supports over twenty mainstream and multimodal models.
The vLLM‑Kunlun Plugin, built on the vLLM hardware‑plugin RFC, lets developers deploy any major large language model on Baidu's Kunlun XPU instantly without modifying vLLM core code, dramatically shortening migration time, providing high‑performance fusion operators, and offering open‑source tools for precision verification and profiling.
This guide explains how to unleash vLLM’s full potential by optimizing batch size, leveraging 4‑bit quantization, tuning concurrency parameters, planning capacity with token‑per‑second metrics, and implementing robust monitoring to balance latency, cost, and scalability in production deployments.
SpecExit combines speculative sampling with a lightweight draft model to predict early‑exit signals, shortening large‑reasoning model chains by up to two‑thirds and achieving up to 2.5× end‑to‑end inference acceleration on vLLM without sacrificing accuracy.
This article details the cost and speed challenges of embedding vectors in large‑scale log scenarios, analyzes inference framework choices, describes GPU utilization, priority queuing, and pipeline redesigns, and reports a 16‑fold throughput increase and dramatically lower per‑request costs.
This guide explains what vLLM is, how its PagedAttention architecture boosts LLM throughput, provides step‑by‑step installation commands, showcases core examples for text generation, chat, embedding and classification, and details advanced performance features such as quantization, LoRA support, and distributed parallelism.
This article walks through installing and configuring vLLM for local large language model inference, compares it with Ollama and LM Studio, details environment setup, model download, testing scripts, and shows how to expose an OpenAI‑compatible API for production use.
This article examines how Volcano Engine's Elastic Instant Cache (EIC) tackles the memory bottleneck, high‑concurrency latency, and cross‑node coordination challenges of large language model inference by decoupling storage and computation, pooling resources, and applying layered optimizations, ultimately boosting AI inference efficiency, scalability, and cost‑effectiveness across various deployment scenarios.
A recent migration of a multimodal image inference system from an internal network to a cloud environment revealed that NVLink bridges dramatically improve multi‑GPU inference speed by reducing inter‑GPU communication overhead, while tensor‑parallel and data‑parallel strategies each have distinct trade‑offs for model deployment.
This tutorial walks through deploying vLLM inference services on a GPU‑enabled Kubernetes cluster using llmaz, configuring Higress as an AI gateway for traffic control, observability, and fallback model switching, and demonstrates end‑to‑end request testing.
This article walks through deploying the high‑performance vLLM LLM inference framework, covering GPU and CPU backend installation, environment setup, offline and online serving, API usage, and a performance comparison that highlights the ten‑fold speed advantage of GPU over CPU.
This tutorial walks you through using a one‑click script to create a GPU‑enabled Kind Kubernetes cluster, evenly distribute GPU resources across nodes with nvkind, install necessary drivers and toolkits, deploy a vLLM‑served large language model, and verify its operation, all on a local or cloud environment.
This article shares a detailed, first‑hand analysis of the technical challenges, framework choices, memory management, weight conversion, precision alignment, and efficiency optimizations encountered while building reinforcement‑learning pipelines for a 671‑billion‑parameter model using the verl ecosystem.
This article explains how an asynchronous RLHF pipeline built on vLLM, Ray, and OpenRLHF dramatically reduces training bottlenecks by decoupling inference, environment interaction, and model updates, and provides detailed implementation code and design choices for scalable reinforcement learning.
This article provides a comprehensive comparison of seven popular large‑language‑model inference engines—Transformers, vLLM, Llama.cpp, SGLang, MLX, Ollama and others—detailing their core features, performance characteristics, hardware compatibility, concurrency support, and ideal use‑cases, plus practical installation guidance for Xinference.
SimpleAR demonstrates that a vanilla autoregressive model with only 0.5 B parameters can generate high‑fidelity 1024×1024 images, covering pretraining, supervised fine‑tuning, and reinforcement learning, achieving competitive GenEval (0.59) and DPG‑Bench (79.66) scores while reducing inference time to about 14 seconds with vLLM and KV‑cache optimizations.
This guide shows how to use the lightweight OpenStation platform to install, configure, and launch the DeepSeek‑R1 large‑model on a personal server in under 15 minutes, covering zero‑code deployment, resource management, inference engine selection, and integration with CherryStudio.
This article presents a step‑by‑step guide for deploying and optimizing large‑language‑model inference across multiple GPU‑enabled nodes using ACK Gateway with Inference Extension, vLLM’s tensor‑ and pipeline‑parallel techniques, and Kubernetes resources such as LeaderWorkerSet, PVCs, and custom routing policies, followed by performance benchmarking and analysis.
After deploying the full‑precision DeepSeek‑R1 model on a 2×8‑GPU ACS cluster, repeated stress tests showed GPU memory usage continuously rising without release; this article details the investigation, reproduces the behavior, examines vLLM logs, Prometheus metrics, and reveals PyTorch’s caching allocator as the root cause, offering mitigation tips.
The article evaluates Augment Code, an AI‑powered coding assistant with 200K token context, persistent memory, multimodal input, and top SWE‑bench scores, walks through its installation, explores its use on vllm and PagedAttention, demonstrates adding a new model and auto‑generating a WeChat mini‑program, and compares its capabilities and speed to Cursor.
This article explores the observability challenges of AI inference services, outlines a comprehensive Prometheus‑based metric collection strategy, and demonstrates practical monitoring implementations for Ray Serve, vLLM, GPU resources, and custom metrics to build stable, high‑performance inference pipelines.
