#KV Cache optimization
284 related articles
·4 min
KV Cache Saves 20x on Costs: The Underlying Principles and Practical Tips for LLM Inference Optimization
Deep dive into how KV Cache reduces LLM API costs by 20x. From Transformer attention matrix multiplication overhead to prompt caching best practices, understand the fundamentals of AI inference cost optimization.
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·3 min
Deep Dive into Inkling: The First Frontier Open-Source Multimodal LLM from the West
Thinking Machines releases Inkling, an open-source multimodal LLM with near-trillion MoE parameters, 1M token context, Apache 2.0 license. Deep dive into architecture, benchmarks, and pricing.
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·4 min
Colibri: Running a 744-Billion-Parameter LLM on an Ordinary Laptop
Colibri uses MoE hot-cold separation and 4-bit quantization to run 744B-parameter models like GLM 5.2 on consumer hardware. Learn about its three-tier memory architecture and speculative decoding.
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·4 min
Kimi K3 Deep Dive: 2.8 Trillion Parameter Open-Source Model Closes the Gap with Closed-Source Frontier for the First Time
Moonshot AI releases Kimi K3 open-weight model with 2.8T parameters and 1M token context. Our deep dive covers coding, 3D dev, agent capabilities, and safety concerns.
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·4 min
Kimi K2/K3 Technical Speculation: Deep Dive into RL Hyperparameters and Multi-Teacher Distillation
Deep analysis of RL hyperparameter tuning challenges and 9-policy multi-teacher distillation in Kimi K2/K3 training, exploring the shift from scale to training craft.
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·2 min
Gemini Always Crashes on the 16th Turn? Error 1076 Root Cause Analysis and Workarounds
Google's Gemini consistently triggers Error 1076 on the 16th conversation turn, regardless of context size. Analysis points to a session state management defect, with three workarounds provided.
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·3 min
Apple Silicon LLM Inference Speed Benchmarks: A Complete Guide to Local Large Model Performance on Mac
In-depth analysis of Apple Silicon local LLM inference speed benchmarks covering M-series memory bandwidth, model quantization, MLX framework optimization, and Mac configuration guidance.
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·4 min
Choosing the Wrong Agent Ruins Everything: Why the Same Model Can Perform Like Two Completely Different AIs
The same LLM API performs drastically differently under different Agent frameworks. Through a real database crash case, this article analyzes why choosing the right Agent matters more than switching models.
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·2 min
DeepSeek V4 Official Release Imminent: What 1.6 Trillion Parameters Really Means
DeepSeek V4 official release is imminent. V4 Pro features 1.6 trillion parameters with million-character context. Explore the D-SPARK acceleration framework, dynamic pricing, and V4's leap to production-grade AI.
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·4 min
The Dao of DeepSeek: Liang Wenfeng on Vision, Restraint, and the Philosophy of Open Source
Analysis of DeepSeek founder Liang Wenfeng's rare investor dialogue, exploring the company's vision-driven culture, strategic restraint toward AGI, and open-source philosophy in the US-China AI race.
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·3 min
Deep Dive into the Kimi-K3 Technical Report: A Comprehensive Analysis of Architecture, Long-Context Processing, and Reasoning Capabilities
Deep dive into Moonshot AI's Kimi-K3 technical report, analyzing its long-context processing, MoE architecture, reasoning improvements, and its position in global AI competition.
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·3 min
Deep Dive into the Kimi-K3 Technical Report: Architecture, Long Context, and Reasoning Capabilities
Deep analysis of Moonshot AI's Kimi-K3 technical report covering long context processing, MoE architecture, reasoning capabilities, and China's position in the global AI competition.
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·2 min
Google Flash and Flash-Lite Models Upgraded and Live: A New Lightweight, High-Efficiency AI Option
Google rolls out upgraded 3.6 Flash and 3.5 Flash-Lite models. Learn about the Flash series' positioning, upgrade highlights, and value for developers.
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·2 min
Kimi-K3 Lands on HuggingFace: A Full Breakdown of Moonshot AI's Open-Source Strategy
Moonshot AI's Kimi-K3 model is set to launch on HuggingFace. A deep dive into its architecture, licensing, community reactions, and strategic implications for the global open-source AI landscape.
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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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·4 min
Gemini 3.5 Pro Hands-On: No More Lazy Code Generation, Complete Projects in a Single Output
A Reddit user tested Gemini 3.5 Pro in Arena and found it generates 20+ files with hundreds of lines of code per file in a single pass, with no lazy shortcuts or placeholders.
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·3 min
Kimi K3 Local Deployment Guide: Storage Requirements for q4 and q8 Quantization Explained
Detailed analysis of Kimi K3 quantization deployment options, comparing q4 vs q8 storage requirements, precision trade-offs, and hardware configurations for local self-hosting.
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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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·2 min
D-Flash: How Diffusion Drafting Delivers 3.5x Faster LLM Inference
D-Flash solves the autoregressive drafter latency bottleneck in speculative decoding via fast diffusion parallel drafting and target feature KV injection. 16 tokens in just 6ms, up to 3.5x speedup on HumanEval, beating EAGLE3 and MTP.
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·3 min
D-Flash: The Secret Behind 3.5x LLM Inference Speedup with Diffusion Drafting
D-Flash uses fast diffusion parallel drafting and target feature KV injection to solve the autoregressive Drafter latency bottleneck in speculative decoding. 16 tokens in just 6ms, up to 3.5x speedup on HumanEval, surpassing EAGLE3 and MTP.
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