硬體

AI 晶片、GPU、資料中心、邊緣裝置與硬體安全。

最新模型

Faster IndexTTS-2 Ports Its Autoregressive Speech Pipeline to TensorRT, Delivering Up to 3.6× End-to-End Speedup

A new study restructures IndexTTS-2’s GPT, Diffusion Transformer, and vocoder using TensorRT and TensorRT-LLM, while adding streaming and batched inference. Experiments on the English and Chinese Seed-TTS benchmarks show up to a 5× speedup for GPT, but the currently available public materials remain insufficient to directly reproduce the complete optimization pipeline.

推論系統

MoAI Showcases GLM-5.1 on 32 MI300X GPUs, Integrating Heterogeneous GPU Routing and Disaggregated Inference into a Single Service

Moreh demonstrated a distributed inference service running GLM-5.1 across four nodes equipped with 32 AMD MI300X GPUs, with live displays of TTFT, TPOT, and GPU utilization. MoAI further claims it can split prefill and decode across GPUs from different vendors, but the latest demonstration did not disclose complete throughput data or a reproducible configuration.

代理工具

Prime Intellect Unifies 365,000 Agent Tasks, Giving Evaluation and Reinforcement Learning a Shared Execution Interface

Prime Intellect has consolidated software engineering, terminal operation, and web search datasets into 23 tasksets accessible through a unified API for agent evaluation and reinforcement learning. The platform also delays injecting tests and grading materials to reduce reward hacking, but grading still shares a sandbox with the agent, so isolation remains incomplete.

AI 基礎設施

GigaToken Accelerates BPE With Rust, SIMD, and Direct File I/O, Reaching 24.53 GB/s in a GPT-2 Tokenization Benchmark

The open-source GigaToken 0.9.0 project reoptimizes pre-tokenization, BPE merging, and the data input path, claiming a 989× speedup over Hugging Face Tokenizers on a dual-socket, 144-core AMD EPYC server. Its top result depends on the native API, an 11.9 GB corpus batch, and highly parallel hardware, and cannot be directly extrapolated to short online requests.

最新模型

Solar Open 2 Supports a Million-Token Context with Hybrid Linear Attention, Activating Just 15B of Its 250B MoE Parameters per Token

South Korea’s Upstage has released Solar Open 2 for long-horizon agents, combining a 250-billion-parameter MoE, hybrid attention, and a 1-million-token context window in a single model. The company says the quantized model can be deployed on two NVIDIA H200 GPUs, though current evidence for its performance and long-context stability comes primarily from the team’s own testing.