Popular - Hardware(Build)
Updated: Monday, May 18Hardware
- Transformer-only inference appliances and custom accelerator silicon.
- Atlas systems ship for very large open and proprietary models.
- OpenAI-style API after uploading Hugging Face model weights.
- Positions on lower power per token than GPU racks on steady loads.
- Inference accelerators and RebelServer rack-scale AI infrastructure.
- REBEL-Quad and ATOM-Max silicon tuned for mixed-precision LLM serving.
- SDK support for PyTorch, vLLM, Triton, and Hugging Face stacks.
- Korean chip vendor targeting sovereign and enterprise inference racks.
- Inference NPUs such as Warbler and RNGD for data-center LLMs.
- Targets throughput per watt on steady production endpoints.
- Compiler path from ONNX and common frameworks to its tiles.
- Korean vendor pushing inference chips into more global clouds.
- Wafer-scale processors for very large neural network training.
- Clusters when single-GPU memory is too small for the model.
- Software compiles PyTorch and JAX to Cerebras kernels.
- Common in labs, pharma, and frontier-model research groups.
- ASIC family tuned for transformer inference, not general graphics.
- Trades GPU flexibility for dense matrix throughput on fixed models.
- Sohu-class parts aimed at steady LLM serving endpoints.
- Inference-first silicon for teams optimizing cost per token.
- Reconfigurable dataflow RDUs for private AI training and inference.
- DataScale appliances bundle chips with the SambaFlow stack.
- Targets enterprises running models inside regulated data centers.
- Full-stack vendor path instead of hand-built GPU farms.
- Wafer-scale processors for very large neural network training.
- Clusters when single-GPU memory is too small for the model.
- Software compiles PyTorch and JAX to Cerebras kernels.
- Common in labs, pharma, and frontier-model research groups.
- Colossus IPU accelerators with the Poplar software stack.
- Many small cores and large on-chip memory versus typical GPUs.
- Data center cards and POD systems with PyTorch and TensorFlow ports.
- Alternative clusters for graph-heavy or large-batch ML workloads.
- LPU inference accelerators built for low-latency LLM serving.
- Cloud API and racks for high-throughput chat and code endpoints.
- Uses a time-scheduled processor stack instead of CUDA GPUs.
- Popular when teams optimize milliseconds per token in production.