#PagedAttention
152 related articles
·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.
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·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.
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·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.
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·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.
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·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.
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·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.
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·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.
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·3 min
Moonshot AI Open-Sources FlashKDA: A Deep Dive into High-Performance CUDA Kernels for KDA Attention
Moonshot AI open-sources FlashKDA, providing high-performance CUDA kernels for Kimi Delta Attention. Learn about its technical principles, performance gains, and value for long-context training and inference acceleration.
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·3 min
Moonshot AI Open-Sources FlashKDA: A Deep Dive into High-Performance CUDA Kernels for KDA Attention
Moonshot AI open-sources FlashKDA, providing high-performance CUDA kernels for Kimi Delta Attention. Explore its technical principles, performance gains, and value for long-context training and inference.
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·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.
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·3 min
Opus 5 Hands-On: Powerful but Slow — Inference Speed Becomes the New AI Battleground
An AI practitioner tests Opus 5: powerful capabilities but painfully slow inference. Deep analysis of how LLM competition is shifting from intelligence to inference speed.
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·3 min
HuggingHack Update: S3 Storage, Dual-Engine Scheduling, and GGUF Inspection Get Major Upgrades
HuggingHack releases major updates with S3/MinIO storage, Ollama + vLLM dual-engine scheduling, GGUF inspection, and local accounts for enterprise-grade local LLM management.
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·3 min
DeepSeek-OCR Deployment and Fine-Tuning: A Complete Hands-On Guide
Master the full DeepSeek-OCR deployment and fine-tuning workflow: vLLM inference deployment, efficient Unsloth fine-tuning, dataset preprocessing, LoRA training, validation, and RAG vector database integration.
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·3 min
DeepSeek Open-Sources DeepSpec: A Full-Stack Speculative Decoding Library with 60–85% Inference Speedup
DeepSeek open-sources DeepSpec, a full-stack speculative decoding library delivering 60–85% inference speedup and up to 661% throughput gains in production. MIT license, 9 checkpoints, 9 benchmarks.
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·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.
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·3 min
LangChain v1.3 Hands-On Tutorial: Deep Dive into Agents, RAG, and Local Model Deployment
Deep dive into LangChain v1.3: compare LangChain, LangGraph, and DeepAgent paradigms, explore RAG pipelines, multi-agent systems, and local LLM deployment for enterprise AI apps.
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llmfit: One Command to Check Which Loc…
·2 min
llmfit: One Command to Check Which Local LLMs Your Hardware Can Run
llmfit is an open-source Rust CLI tool that predicts whether your local hardware can run a given LLM — no model download required. Covers hundreds of models and backends like Ollama and llama.cpp.
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·3 min
Kimi K3 Hands-On: How a 2.8-Trillion-Parameter Open-Source Model Beat Fable5
A US engineer's live test of Kimi K3: 2.8T parameters, 1M token context, 87% audience vote over Fable5 in game generation. Full report covering benchmarks, speed, and code debugging.
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·3 min
V100 GPU Showdown: 4×16G PCIe vs. 2×32G SXM — Which Gives You More Bang for Your Buck?
Benchmarking 4×V100 16G PCIe vs. 2×V100 32G SXM adapter for local LLM inference. Prefill speed, decode speed, power limits, and bandwidth bottlenecks analyzed.
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·2 min
Qwen3 Fine-Tuning in Practice: A Complete Guide to Local Deployment and Domain Training
A hands-on guide to LLM fine-tuning: from understanding model weights to local Qwen3 deployment, dataset preparation, and domain-specific training. Build a complete AI engineering skill set.
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