Running Qwen 3.6 27B Locally on the RTX Pro 4500 Blackwell
Benchmark results from running Qwen 3.6 27B locally on NVIDIA's professional-grade GPU, the RTX Pro 4500 Blackwell, were shared on the r/LocalLLaMA community on Reddit.
The RTX Pro 4500 Blackwell comes with 48GB of memory and Compute Capability 12.0. Under a CUDA 13.1 environment, the user ran the quantized model Qwen3.6-27B UD-Q5_K_XL with llama.cpp and confirmed the following performance:
Prompt processing (pp512)**: 1751.21 t/s
Text generation (tg128)**: 35.83 t/s
The context size was set to 131,072 tokens (131K), with Flash Attention also enabled. The user has connected this model to Roo Code and uses it for Unreal Engine 5 development.
Just recently there was a report of Qwen3.6-27B running on the RTX 5000 PRO, and with 48GB-class VRAM on Blackwell-generation Pro GPUs, fast local execution of 27B-parameter models is becoming a realistic option. In particular, prompt processing speeds exceeding 1,700 t/s can be considered practical for coding assistant use cases.
Detecting DeFi Attacks from One Million Transaction Graphs with a Vision-Language Model
An effort to render blockchain transaction data as images and detect attack patterns with a Vision-Language Model (VLM) is attracting attention.
A DePIN security oracle project called "Sigui" collected 1.87 million real on-chain transactions from three chains—Ethereum, Arbitrum, and Polygon—and generated one million transaction graph images. Each graph is annotated with one of three labels: `DRAIN_STAR`, `MIXING_CHAIN`, or `NORMAL`.
Dataset generation used 20-core parallel processing on an AMD MI300X and completed in about 1 hour and 15 minutes. The team is now fine-tuning Qwen2-VL-7B with LoRA to develop a model that can visually detect attack patterns from transaction graphs without relying on static rules.
The dataset has been released on HuggingFace under the MIT license. Alongside it, ERC-8259, an Ethereum standard for managing AI agent identity and threats, has also been proposed.
As an approach that compensates for the limitations of traditional rule-based detection, the idea of feeding the graph structure itself into a VLM as an "image" is intriguing. Beyond DeFi security, applications in other fields such as network anomaly detection are also expected.
From the Japanese Engineer Community
On Qiita, practical articles on AI usage stood out.
One article introduced the technique of "consolidating requirements into a single instruction" as an effective way to use Claude Code. The report that prompts integrating all requirements deliver better accuracy than fragmented chat instructions is a useful reference for working with autonomous AI agents in general.
There was also a freelance engineer's three-day record of building Obsidian into a "second brain" in combination with Claude, as well as the announcement of "Praxia" (Apache 2.0), an open-source multi-agent foundation that automatically converts individual tacit knowledge into an organizational knowledge base. AI integration in engineering workflows is steadily taking shape as concrete tools.