Distributed Training Frameworks¶
Training frontier models requires distributing computation across thousands of GPUs. These frameworks handle the parallelism, memory optimization, and communication needed to train models with billions (or trillions) of parameters.
Distributed Training Frameworks¶
| Name | Description | Links |
|---|---|---|
| ColossalAI | Making large AI models cheaper, faster, and more accessible with efficient parallelism techniques. | colossalai.org |
| DeepSpeed | Microsoft's deep learning optimization library for distributed training and inference. | deepspeed.ai |
| Megatron-LM | NVIDIA's research framework for training large-scale Transformer models with model and data parallelism. | - |
AI Compute Infrastructure¶
The physical backbone of the AGI race. As models scale to trillions of parameters and inference demand explodes, energy, hardware, and data center capacity become the defining bottleneck.
| Name | Description | Links |
|---|---|---|
| Groq | LPU (Language Processing Unit) inference engine delivering record-breaking tokens/second. Custom ASIC designed from the ground up for LLM inference. | groq.com |
| Crusoe Energy | Clean-energy AI data centers powered by stranded natural gas and renewables. Purpose-built GPU clouds for AI training and inference. | crusoe.ai |
| Cerebras | Wafer-Scale Engine (WSE) -- the largest chip ever built -- designed for AI training. CS-3 system eliminates memory bottlenecks with 44GB on-chip SRAM. | cerebras.ai |
| SambaNova | Reconfigurable Dataflow Architecture (RDA) for enterprise AI. Purpose-built hardware that adapts to model topology. | sambanova.ai |
| Meta MTIA | Meta Training and Inference Accelerator -- custom AI silicon designed for Meta's recommendation and generative AI workloads. 4 chip generations in 2 years, powering AI for billions of users. | ai.meta.com |
| Google TPU (Trillium) | Google's 6th-gen TPU (Trillium, 2024) with 4.7x compute per chip improvement over TPU v5e. Powers Gemini training and inference at scale across Google's AI fleet. | cloud.google.com/tpu |
| NVIDIA Blackwell (B200/GB200) | NVIDIA's 2024 GPU architecture: 208B transistors, 2nd-gen Transformer Engine with FP4, 30x faster inference on LLMs vs. Hopper. GB200 NVL72 rack delivers 1.4 exaFLOPS. The dominant hardware for frontier model training. | nvidia.com/blackwell |
Energy, Data Centers & Physical Constraints¶
The AGI race is ultimately constrained by physics: energy, cooling, and chip fabrication. As training runs scale from gigawatt-hours to hundreds of gigawatt-hours, the physical infrastructure becomes as important as the algorithms.
| Challenge | Current State | Links |
|---|---|---|
| Gigawatt-scale data centers | Microsoft, Meta, Google, Amazon, and xAI are all building or planning 1-5 GW data centers. xAI's Memphis "Colossus" cluster: 100k H100s. Microsoft's $100B Stargate project with OpenAI. | Industry announcements |
| Nuclear-powered AI | Microsoft restarted Three Mile Island Unit 1 for AI power. Amazon, Google, and Oracle securing nuclear power for data centers. SMR (Small Modular Reactor) partnerships proliferating. | News |
| The Memory Wall | Context window expansion (4K -> 128K -> 1M -> 10M tokens) functions as "working memory" for AGI. Llama 4 Scout: 10M tokens. Gemini 1.5: 2M. But attention scales quadratically -- memory-efficient architectures (Infini-Attention, Mamba, RWKV) are essential. | See Reasoning Papers |
| Training cost trajectory | GPT-4: ~$100M. Llama 3 405B: ~$30M. DeepSeek-V3: $5.5M. Efficiency gains (MoE, distillation, FP8/FP4) are democratizing frontier training. | Model technical reports |