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The specialized-data market is becoming AI's new bottleneck
Mercor’s revenue crossed $2B as frontier labs pay premium prices for expert labeled data, turning data providers into AI’s new bottleneck.

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Tensor Parallelism in Large Language Models: How Megatron-LM Partitions Multi-Layer Perceptrons and Attention Heads
Tensor Parallelism in Large Language Models: How Megatron-LM Partitions Multi-Layer Perceptrons and Attention Heads Training and serving modern large language models requires navigating severe hardware memory and compute constraints. While standard Distributed Data Parallelism (DDP) replicates the entire model across multiple accelerators, modern frontier architectures containing tens or hundreds of billions of parameters exceed the physical memory capacity of any single GPU. Even with 80 GB o
1 minLocal LLM Inference on Apple Silicon: Architecture, Unified Memory, and Serving Benchmarks for MLX, llama.cpp, and Ollama
Local large language model (LLM) serving on consumer hardware has historically faced a hard trade-off between memory capacity and execution bandwidth. Discrete consumer GPUs offer high memory bandwidth (up to 1,008 GB/s on an Nvidia RTX 4090) but are capped at 24 GB of VRAM, requiring model sharding or quantization to fit models beyond 14 billion parameters. Apple Silicon platforms bypass this capacity ceiling through a Unified Memory Architecture (UMA), where the CPU, GPU, and Apple Neural Eng
1 minMistral Expands Platform to Host Third-Party Open Weights Starting with GLM-5.2
Mistral AI has broadened its API platform to host external open-weight foundation models, beginning with Zhipu AI's GLM-5.2. The move marks a strategic shift for the Paris-based AI company from serving only in-house architectures (such as Mistral Small, Mistral Medium, Mistral Large, and Voxtral) toward operating as a sovereign managed inference hub for third-party open weights. The integration introduces GLM-5.2 under the model identifier zai-glm-5-2 in public preview. The model is hosted with
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