@ankitnarayan1
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zeenews.india.com From vision to impact: Trusted leaders to look out for in 2026 Emerging leaders across finance, fitness, education, hospitality, AI, healthcare, branding, and construction are redefining growth through innovation, technology, and human-centric strategies 
tech.hindustantimes.com Brands to watch in 2026: Redefining the future of business I implemented Google's TurboQuant paper (ICLR 2026) in llama.cpp with Metal kernels for Apple Silicon.
4.9× KV cache compression. Working end-to-end on M5 Max with Qwen 3.5 35B MoE and Qwopus v2 27B.
Speed needs work (unoptimized shader), compression target met.
Repo: github.com/TheTom/turboqu…
**Note**: as you'll see from the git when I saw "I" it's in conjunction with claudecode and codex. Just lots of steering and babysitting.View quoted post on X
🧵As 2026 unfolds, sparse MoE models are emerging as the new backbone for high-throughput inference and Agent workloads. Step 3.5 Flash from StepFun @StepFun_ai stands out with its efficient attention mixture design.
Here’s a technical deep dive from Zhihu contributor kaiyuan 👇
🤖 Model Overview
• Open-source MoE LLM built for high-throughput inference & Agent scenarios
• Matches or exceeds leading models in reasoning, coding, and Agent benchmarks
• Delivers speed-quality balance via sparse MoE routing + Multi-Token Prediction (MTP)
📐 Core Architecture
• Backbone: Transformer + MoE | Total params: ~196B | Active params: 11B/token
• Attention mix: GQA + Sliding Window Attention (SWA) + Full Attention
• Routing: 288 experts | Top-8 activation per token
• Context window: 256K (262144 max sequence length)
⚙️ Key Configs (config.json)
• Layers: 45 | Hidden dim: 4096 | Attention heads: 64
• Sliding window: 512 | Max sequence length: 256K
✨ Standout Features
• Sparse MoE Routing: 288 experts with Top-8 selection → cuts compute without losing capacity
• Dual Attention Mechanism: SWA for efficient local modeling (512-window) + Full Attention for global context
• MTP Acceleration: Faster decoding for real-world interactive throughput
✅ Final Takeaway
Step 3.5 Flash proves that sparse MoE architectures can deliver enterprise-grade performance for long-context, high-throughput applications without proportional resource growth.
#AI #Engineering #Tech #LLM #Agent #StepFun
🔗 Full article(CN):zhuanlan.zhihu.com/p/202116139986…View quoted post on X
