#vLLM vs SGLang

15 related articles

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 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

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.

Jul 28, 2026·3 min

A 9B Model Beats Frontier LLMs After Just $500 in RL Fine-Tuning

Just $500 in RL fine-tuning enables a 9B open-source model to outperform frontier LLMs on catalog review tasks. Analysis of when small-model RL works and its enterprise implications.

Jul 25, 2026·3 min

Laguna S 2.1: The Open-Source Coding Model That's 14x Smaller Yet Stronger

Poolside releases Laguna S 2.1, an open-source agentic coding model: 118B total params with only 8B active, scoring 40.4% on DeepSWE — ~4.5x DeepSeek V4 Pro Max. Supports 1M-token context, deployable on a single workstation.

Jul 24, 2026·6 min

Hands-On with Panel AI On-Premises Deployment: One-Click Enterprise Adoption and Unified Compute Cluster Scheduling

In-depth review of Panel AI v1.1.1: second-level installation, no-public-IP networking, batch compute cluster management. Learn how enterprise AI on-premises deployment barriers are dramatically lowered.

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, 2026·3 min

TB/s Memory Tier Accelerates SGLang: How X-Mem Reduces TTFT by 6.7x

Netpreme integrates X-Mem™ MPU into SGLang HiCache, achieving up to 6.7× TTFT reduction and 33–50% TPS gains at 98% prefix cache hit rates. Here's the technical breakdown.

Jul 11, 2026·3 min

The Software Compounding Effect: The Real Deciding Factor in AI Infrastructure Competition

Beyond the GPU arms race, software is becoming AI infrastructure's true compounding engine. A deep dive into optimization stacking, the CUDA ecosystem moat, and how open source scales individual breakthroughs into community wealth.

Jul 11, 2026·5 min

Ollama Raises $65M Series B: 85% of Fortune 500 Have Already Deployed Local LLMs

Local LLM tool Ollama closes a $65M Series B, bringing total funding to $88M. With 9M developers and 85% of Fortune 500 having deployed internally, this deep dive explores why enterprises embrace local LLMs: compliance, Agent cost savings, and open-source ecosystem.

Jul 10, 2026·2 min

Tencent Hunyuan V3 Open-Sourced: Full Breakdown of the 295B MoE Architecture — 256K Context Is Its Biggest Weakness

Tencent open-sources Hunyuan V3: 295B MoE model with 21B active params, claims to beat DeepSeek-V4-Pro. Supports FP8, vLLM/SGLang — but 256K context and no multimodal are key drawbacks.

DeepSeek-V3.2 Released: Coding and Math Capabilities Join the Global Top Tier

Tech Frontiers

Jun 3, 2026·2 min

DeepSeek-V3.2 Released: Coding and Math Capabilities Join the Global Top Tier

DeepSeek-V3.2 released with coding, math, and Agent capabilities matching Gemini 3.0 Pro, setting new open-source SOTA. Detailed analysis of performance gains, use cases, and deployment tips.

Decoding LLM Naming Conventions: Parameter Counts, Quantization Formats & VRAM Requirements Quick Reference

Tutorials

May 27, 2026·3 min

Decoding LLM Naming Conventions: Parameter Counts, Quantization Formats & VRAM Requirements Quick Reference

Decode LLM naming conventions, understand 32B parameters & AWQ/GGUF quantization formats, with 4-bit VRAM estimation formulas, MOE model pitfalls, and model selection by GPU tier.

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.

Kimi K2 Official Verification Tool K2-Vendor-Verifier: One-Click API Vendor Model Accuracy Detection

Tech Frontiers

May 13, 2026·2 min

Kimi K2 Official Verification Tool K2-Vendor-Verifier: One-Click API Vendor Model Accuracy Detection

Moonshot AI open-sources K2-Vendor-Verifier to verify third-party Kimi K2 API vendor inference accuracy. Learn how this tool helps developers detect over-quantization, model substitution, and other API market risks.