Make AI models faster, smaller, and less expensive to run.
ByteShape enables organizations to deploy more capable AI on existing hardware while reducing latency, memory requirements, energy consumption, and infrastructure cost.
Deploy more capable AI on the hardware you already have
Improve AI economics
Reduce infrastructure, memory, and energy requirements.
Use existing hardware
Deploy larger or higher-quality models on available systems.
Own the deployment
Run in cloud, on-premises, at the edge, or in sovereign infrastructure.
Automated Model + Hardware Optimization
ByteShape automatically optimizes numerical precision, value representation, and execution code for each target model, workload, and hardware platform. Our system explores fine-grained datatype and quantization choices across tensors and value groups, balancing model quality against customer-defined deployment objectives.
Why ByteShape
Beyond fixed recipes and manual tuning
Systematically learns a broader optimization space using measured quality and performance.
Built around your deployment target
Optimizes against the quality, latency, throughput, memory, energy, and cost targets that matter.
Models, formats, workloads, and devices
Supports integer, floating-point, and microscaling representations across diverse architectures (not only LLMs) and targets.
Designed to adapt as AI changes
Operates at the level of tensors, datatypes, data movement, and compute kernels — not one fixed model family.
Built for repeatable deployment across the AI stack
Ship optimized releases across more devices and price points.
Improve utilization and offer stronger performance per dollar.
Deploy capable AI while retaining control of systems and data.
Proven Technical Foundation
Peer-reviewed research at MLSys, demonstrated from edge devices to high-end GPUs. Commercial model: annual enterprise subscriptions and optimization engagements.