Compute, Hardware & Quantization¶
You can't build ASI without massive, optimized infrastructure. The AGI race is ultimately a compute race -- constrained by silicon, energy, and the physics of chip fabrication.
AI Accelerators & Custom Silicon¶
| Name | Description | Links |
|---|---|---|
| NVIDIA Blackwell (B200/GB200) | 208B transistors, FP4 Transformer Engine, 30x inference vs Hopper. GB200 NVL72: 1.4 exaFLOPS. | nvidia.com/blackwell |
| Google TPU (Trillium) | 6th-gen TPU (2024), 4.7x compute vs v5e. Powers Gemini training and inference. | cloud.google.com/tpu |
| Groq LPU | Custom ASIC for record tokens/sec inference. Deterministic execution, no batching overhead. | groq.com |
| Cerebras WSE-3 | Wafer-Scale Engine, largest chip ever. 44GB on-chip SRAM eliminates memory bottleneck. | cerebras.ai |
| SambaNova RDU | Reconfigurable Dataflow Architecture adapts to model topology. Enterprise AI silicon. | sambanova.ai |
| Meta MTIA | Custom silicon for recommendation and generative AI at Meta scale. | ai.meta.com |
| Intel Gaudi 3 | Habana Labs AI accelerator, competitive price-performance for training and inference. | habana.ai |
| AMD Instinct MI300X | 192GB HBM3, competitive with H100 for inference. Growing adoption. | amd.com |
| AWS Trainium2 / Inferentia2 | Amazon's custom ML chips for training and inference. Powers Bedrock and SageMaker. | aws.amazon.com |
Model Quantization & Efficiency Tools¶
Making frontier models accessible. Quantization compresses models from 16-bit to 4-bit or lower with minimal quality loss, enabling local deployment and reducing inference costs by 4-8x.
| Tool | Description | Links |
|---|---|---|
| bitsandbytes | 8-bit and 4-bit quantization for PyTorch. QLoRA backbone. HuggingFace integrated. | GitHub |
| GPTQ | Post-training quantization via Hessian approximation. 3-4 bit, minimal quality loss. | Paper, GitHub |
| AWQ (Activation-Aware Weight Quantization) | Protects salient weights by activation patterns. Often better than GPTQ at same bit-width. | Paper, GitHub |
| llama.cpp / GGUF | C/C++ inference with GGUF format. Runs LLMs on CPU and consumer GPUs. Local LLM backbone. | GitHub |
| ExLlamaV2 | Optimized GPTQ inference for consumer GPUs. Mixed-precision EXL2 format. | GitHub |
| vLLM Quantization | Built-in AWQ, GPTQ, FP8, INT8 quantization in the vLLM inference engine. | docs.vllm.ai |
| TensorRT-LLM | NVIDIA's LLM optimization with INT4/INT8/FP8, KV cache quantization, layer fusion. | GitHub |
| GGML | Tensor library for ML, enabling LLM inference on commodity hardware. | GitHub |
Neuromorphic & Alternative Compute¶
Beyond von Neumann: hardware architectures inspired by biological neural networks, offering massive energy efficiency gains critical for scaling to AGI.
| Name | Description | Links |
|---|---|---|
| Intel Loihi 2 | Neuromorphic chip with on-chip learning. 1M neurons, event-driven, ultra-low power. | intel.com/neuromorphic |
| IBM NorthPole | 256-core neural inference chip. No external memory during inference. 25x energy efficiency vs GPU. | research.ibm.com |
| BrainScaleS-2 | Analog mixed-signal neuromorphic chip. 1000x faster than biological real-time. | electronic-visions.com |
| SpiNNaker 2 | 1M ARM cores for real-time neural network simulation. Designed for brain simulation. | spinnaker.io |
| Mythic AI | Analog compute-in-memory accelerator. Weights in flash, compute in analog. | mythic.ai |
Decentralized AI Compute¶
Distributing the compute bottleneck. These networks pool GPU resources across the globe for AI training and inference, challenging the centralized data center model.
| Name | Description | Links |
|---|---|---|
| Together AI | Decentralized cloud for open-source model hosting, fine-tuning, and inference. | together.ai |
| Gensyn | Decentralized GPU protocol for ML training with cryptographic verification. | gensyn.ai |
| io.net | Decentralized GPU network aggregating underutilized compute globally. | io.net |
| Prime Intellect | Decentralized training infrastructure. INTELLECT-1, OpenDiLoCo protocol. | primeintellect.ai |
| Akash Network | Open-source decentralized cloud marketplace for compute. | akash.network |
| Vast.ai | GPU rental marketplace connecting idle GPUs to AI workloads. | vast.ai |
Energy & Data Center Scale¶
The AGI race is constrained by physics. Training runs consuming 50-100 GWh, data centers scaling to gigawatts, and the nuclear renaissance for AI.
| Challenge | Current State | Links |
|---|---|---|
| Gigawatt-scale data centers | Microsoft, Meta, Google, Amazon, xAI building 1-5 GW centers. xAI Colossus: 100k H100s. | Industry announcements |
| Nuclear-powered AI | Microsoft restarted Three Mile Island. Amazon, Google, Oracle securing nuclear power. | News |
| Training cost trajectory | GPT-4: ~$100M → DeepSeek-V3: $5.5M. MoE, distillation, FP8 driving costs down. | Model technical reports |
| The Memory Wall | 4K → 1M+ tokens. Attention scales quadratically. Infini-Attention, Mamba, RWKV emerging. | Architecture papers |