AI Models
Model releases, benchmarks, inference and fine-tuning.
AI Model Local Deployment: Qwen Flash Next Through a Growing 131K Session
A four-turn cached-prefix probe on M4 Max reports measured readiness, evaluation and short-decode results—and why cache reuse is not a cold full-window test.
CogVideoX-5B Puts Open Video Generation Within Reach for AI Cloud Infrastructure Companies in India
An expanded analysis of THUDM’s CogVideoX-5B open-weight release, examining its 3D causal VAE, expert transformer architecture, quantization strategies for edge and cloud, and implications for scaling AI cloud infrastructure in India.
Computational Analysis of Patient Language Identifies Suicidal Risk
Expanded article on machine learning and computational analysis of patient language in suicide prevention, incorporating Cincinnati Children's Hospital research, clinical decision support, and community deployment.
LLM Cost Breakdown: When Unbounded Consumption Turns Your AI Bill Into a Weapon
OWASP ranks unbounded consumption sixth on its LLM Top 10. Here is how attackers weaponize your token spend, and what the real LLM cost breakdown looks like when a single malicious session dwarfs legitimate traffic.
AI General Intelligence Definition: Why One Benchmark Score Isn’t Enough
A Sage-A1.5 test shows how thinking mode and sampling can change benchmark outcomes—and why those settings belong beside the score.
MiniCPM-V 4.7 for AI Model Local Deployment: R9700 Results and DSpark Tuning
Measured MiniCPM-V 4.7 results on an AMD Radeon AI PRO R9700, including same-recipe M4 Max comparisons and DSpark draft-depth trade-offs.
How to Deploy an AI Model for Local Agents: M4 Max or Radeon?
Measured MiniCPM-V-4.7 results offer a starting point for choosing a local AI deployment path, but agent workloads demand more than decode speed.
When Sage-A1.5 Thinks: R9700 Results Across Two Modes
A local benchmark report comparing Sage-A1.5 thinking-disabled and greedy thinking-enabled runs, with the mode distinction central to interpreting results.
Practical AI Model Local Deployment: Benchmarking Qwen3.8-27B Multi-Token Prediction Depths on the R9700
Practical AI model local deployment analysis benchmarking Qwen3.8-27B multi-token prediction depths and quantizations on the AMD Radeon AI PRO R9700.
Local Deployment of AI Models on the R9700 Suite: Swift 1.5 vs Stock Qwen3.8-27B
Measured R9700-suite comparison of Swift 1.5 and stock Qwen3.8-27B on MMLU-Pro, GSM8K, and coding tasks, including decode speed, suite wall time, and recommended local run settings.
Radeon R9700 Qwen3.8-27B Quantization Results: Speed, Scores, and Tradeoffs for AI Model Local Deployment
A measured comparison of three Qwen3.8-27B GGUF configurations on a 32 GB Radeon AI PRO R9700, exploring quantization tradeoffs, deployment practices, and benchmark results.
AI Model Local Deployment: Long-Context Fit on the R9700, Measured
A measured local deployment test of 131K-context memory fit, quantization spillover, batch size, and concurrency on the Radeon AI PRO R9700.