#hardware-friendly AI architecture
9 related articles
·4 min
FPGA Softmax Approximation in Practice: A Detailed Guide to Taylor Expansion and Padé Approximation Methods
A detailed guide on efficiently implementing Softmax on FPGAs, comparing Taylor series and Padé approximation methods for accuracy and resource trade-offs, with Python simulation and range reduction techniques.
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Hardware-Software Co-Design: A Guide t…
·2 min
Hardware-Software Co-Design: A Guide to Building Hardware-Friendly LLM Architectures
Explore AI Model Co-Design principles and how hardware-friendly LLM architecture design — covering MoE, GQA, and FP8 quantization — optimizes the accuracy, throughput, and latency trade-off.
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How Fixing Three Bugs Made Qwen3.5-122…
·3 min
How Fixing Three Bugs Made Qwen3.5-122B Run Reliably on Mac Studio
A developer fixed three critical bugs to make Qwen3.5-122B run reliably as a daily driver on Mac Studio. Explore memory management, inference stability, and quantization precision.
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·2 min
Getting Started with Ollama: A Powerful Tool for Deploying Open-Source LLMs Locally
Ollama is a free, open-source LLM management platform that lets you deploy open-source models like DeepSeek locally with one click. It supports macOS, Windows, Linux, and Docker, with both API and CLI modes to build private AI apps at zero cost.
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·2 min
Lingbot World: A 14B Open-Weight World Model with Causal Architecture and Inference Acceleration
Lingbot World is an open-weight world model on Hugging Face with 14B parameters, causal autoregressive architecture, and inference speed optimizations. Explore its architecture and use cases.
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·4 min
Ternlight: A 7MB Browser-Side Embedding Model for Server-Free Semantic Search
Ternlight is a 7MB WebAssembly-based browser-side text embedding model requiring no server or GPU. Explore its tech, use cases, and tradeoffs for private, offline semantic search.
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·3 min
Biological Neurons vs. Artificial Neurons: How Big Is the Computational Gap?
Research shows biological neurons far outperform classical artificial neurons. Explore dendritic computing, temporal coding, and what this means for next-gen AI architecture design.
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Deep Dives·3 min
Complete Guide to LLM Training: Pre-training, SFT Fine-tuning, and Preference Alignment Explained
Complete guide to the three core LLM training stages: pre-training, supervised fine-tuning (SFT), and preference alignment (DPO/PPO), covering LoRA, distillation, quantization, and pruning.
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Product Reviews·2 min
AI Coding Appliance vs Cloud LLMs: Can ¥480K in Annual Fees Buy 4 Local Deployment Solutions?
A deep cost comparison between AI coding appliances and cloud LLM APIs. A 20-person team spending ¥480K/year on tokens can deploy 4 local OnePanel units at ¥99K each, breaking even in 2.5 months.
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