#vLLM

221 related articles

Jul 27, 2026·3 min

DeepSeek-OCR Deployment & Fine-Tuning in Practice: From vLLM Inference to Unsloth Fine-Tuning

Complete guide to DeepSeek-OCR from vLLM inference deployment and Unsloth model loading to fine-tuning, covering cloud server setup, GPU selection, and code examples — all on a single 4090 GPU.

Jul 17, 2026·2 min

vLLM Inference Framework Deep Dive: A Practical Guide to High-Concurrency DeepSeek Deployment

A deep dive into vLLM's five core features — KV cache management, continuous batching, and distributed inference — plus a full setup guide for deploying DeepSeek on a cloud server.

MemStitch Zero-Copy Context Bridging: …

Jul 15, 2026·2 min

MemStitch Zero-Copy Context Bridging: How It Achieves 25x TTFT Speedup in vLLM

A deep dive into how MemStitch's zero-copy context bridging achieves 25x TTFT speedup in vLLM. Covers KV Cache optimization, prefill acceleration, and practical developer value.

Jul 11, 2026·6 min

A Deep Dive into the vLLM Inference Framework: Core Principles of Throughput Optimization and Interview Guide

An in-depth analysis of the vLLM inference framework's core principles: from the meaning of throughput (tokens/s), to the bottlenecks of autoregressive generation, to KV Cache, PagedAttention, and continuous batching.

Jul 11, 2026·2 min

DeepSeek Speculative Decoding Algorithm Merged into vLLM Main Branch — Single-User Token Speed Jumps Dramatically

DeepSeek's speculative decoding algorithm (DSpark) is now merged into vLLM main branch, natively supporting Qwen3 and Gemma. Tests show ~150× single-user token speed gains and ~40–50% throughput improvement.

Jun 6, 2026·3 min

vLLM Deep Dive: How PagedAttention Enables High-Throughput LLM Inference

Deep dive into vLLM's core technologies for high-throughput LLM inference, including PagedAttention memory management, continuous batching, distributed deployment, and comparisons with TensorRT-LLM.

vLLM and SGLang Local Deployment Tutorial: A Practical Guide to 3-8x Performance Gains

Tutorials

May 20, 2026·2 min

vLLM and SGLang Local Deployment Tutorial: A Practical Guide to 3-8x Performance Gains

Complete guide to deploying vLLM and SGLang locally. Compare performance vs LM Studio, deploy in 3 steps with Docker + AI assistant. Covers SGLang vs vLLM selection, 5090 VRAM optimization, and Cherry Studio integration.

Jul 31, 2026·3 min

GPT-5.6 Slashes Prices by 80%: The Industry Logic Behind the AI Price War and Opportunities for Developers

OpenAI's GPT-5.6 series sees massive price cuts—Luna drops 80% to $0.20/M input tokens. Deep analysis of the AI price war's tech drivers, competitive landscape, and impact on developer costs and model selection.

Jul 31, 2026·3 min

GPT-5.6 Price Cut of 80%: The Industry Logic Behind the AI Price War and Opportunities for Developers

OpenAI's GPT-5.6 series sees major price cuts with Luna dropping 80% to $0.20/M input tokens. Analysis of the AI price war's technical drivers, competitive landscape, and impact on developer costs.

Jul 30, 2026·3 min

GPT-5.6 Price Cut by 80%: The Deeper Game Behind the AI Model Price War

OpenAI announces GPT-5.6 Luna 80% cheaper, Terra 20% cheaper, targeting Gemini Flash. Analysis of the AI price war's business logic, Google's moat, Microsoft's hidden gains, and open-source impact.

Jul 30, 2026·3 min

LLM Inference Speed Formula: Memory Bandwidth Determines Your tokens/sec

Why memory bandwidth (GB/s), not VRAM size, determines local LLM inference speed. Includes tokens/sec formula, GPU bandwidth comparison, and a practical card selection framework.

Jul 30, 2026·3 min

LLM Inference Speed Formula: Memory Bandwidth Determines Your tokens/sec

Learn why memory bandwidth (GB/s)—not VRAM size—determines local LLM inference speed. Get the tokens/sec formula, GPU bandwidth comparisons, and a practical card selection hierarchy.

Jul 30, 2026·3 min

Building a Streaming LLM Backend in Go: Deep Dive into the SDK and React Frontend Library

Deep dive into an open-source Go SDK for building streaming LLM backends, covering streaming responses, tool-calling architecture, and companion React library for end-to-end integration.

Jul 30, 2026·2 min

Gemini Suddenly Appeared in Gmail Then Disappeared? A Deep Dive into Gradual Rollout Mechanisms

A Reddit user found Gemini features in Gmail without a subscription, only to lose access a day later. This article explains Google's gradual rollout strategy and AI feature deployment logic.

Jul 30, 2026·3 min

Is LLM Inference Really That Expensive? Breaking Down the Cost Reduction Logic of GPU Compute and Inference Frameworks

Analyzing real LLM inference costs: from B200 GPU compute gains, vLLM framework optimization to MTP multi-token prediction, explaining why serving costs are widely overestimated.

Jul 30, 2026·2 min

Behind the Open-Source Model Frenzy: Who Will Provide Cheap Inference Services?

Open-source LLM weights don't equal low-cost access for developers. This article analyzes the inference service gap in open-source AI and how providers like Together AI and Groq are addressing it.

Jul 30, 2026·2 min

Behind the Open-Source Model Frenzy: Who Will Provide Cheap Inference Services?

Open-source LLM weights don't mean developers can use them cheaply. This article examines the inference service gap in open-source AI and how providers like Together AI and Groq are addressing it.

Jul 29, 2026·3 min

Self-Hosting Kimi K3: Is Spending 20% More on Hardware Worth a 20% Improvement in Task Performance?

Analysis of whether spending 20% more on hardware for self-hosting Kimi K3 to gain 20% task performance improvement is worthwhile, covering inference precision, VRAM optimization, and tiered deployment.

Jul 29, 2026·3 min

Production-Grade LLM Inference Deployment: A Practical K8s Handbook

Complete guide to deploying production-grade LLM inference on Kubernetes, covering GPU scheduling, vLLM engine selection, autoscaling, observability, and cost optimization.

Jul 29, 2026·3 min

Is LLM Inference Actually Profitable? Breaking Down the Cost Structure and Business Logic of Large Model Inference

A deep dive into LLM inference cost structure and profitability models—from GPU throughput, MoE architecture, and KV Cache to scale effects—revealing the business logic behind API price wars.