A Computer architecture built around how AI models compute

Inference-time scaling means more tokens per request and more varied workloads. At that scale, throughput and cost depend less on raw compute and more on how well the model, the compiler and the hardware work together.

When hardware and model don’t match, data spends more time moving than being computed on, and capex buys idle silicon. The fix starts at the abstraction layer between software and hardware.

Deep learning operations do not occur in flat, two-dimensional structures. In transformers, weights and activations are multi-dimensional tensors, and the dominant operation is tensor contraction: matrix multiplication generalized to higher dimensions.

Matrix-multiply accelerators map these tensors onto fixed 2D grids, so the compiler has to flatten, slice and permute them in memory. That adds layout work and extra data movement at every step.

The Tensor Contraction Processor (TCP) resolves this compromise by raising the hardware execution primitive. By accelerating the full tensor contractions as a single, unified hardware operation, the TCP matches the mathematical expression of the workload directly in silicon—treating the tensor operation as a first-class citizen.

In production inference, data movement is the biggest lever on performance per watt. On GPUs, fixed 2D execution grids mean the software stack keeps reshaping and shuffling data across caches and register files, which adds latency and power.

The TCP runs multi-dimensional operations directly on-chip, keeps data in local SRAM, and minimizes movement to and from memory.

Eliminating runtime data orchestration overhead requires an architectural contract that substitutes dynamic hardware scheduling with static, compile-time layout optimization. Legacy architectures introduce hardware-controlled caching and dynamic thread scheduling, creating non-deterministic execution behaviors that force the compiler into reactive mitigation.

On the TCP, execution is structured and predictable, so parallelism and data routing can be planned at compile time. The Furiosa Compiler treats the whole network graph as one optimization problem and minimizes data movement end to end.

The legacy inference software ecosystem is bottlenecked by fragile, hand-crafted operator libraries that rely on manual kernel tuning for isolated tensor geometries and model variations. This heuristic approach fails at scale due to the multivariate combinatorial expansion of dynamic tensor shapes, operator fusions, and advancing micro-architectural revisions. Manual optimization cannot sustain hardware utilization across this multi-dimensional variant space.

The Furiosa Compiler searches layout, tiling and fusion automatically, so most models run well without hand-written kernels and carry over to new chip generations. When you want finer control, TCL and vISA give you kernel-level access.

Fixed-size systolic arrays such as Tensor Processing Units (TPUs) deliver high density but are bound to rigid, fixed-dimension physical systolic arrays built for uniform matrix multiplication. When subjected to the highly variable, asymmetric tensor shapes characteristic of production inference, these rigid grids suffer from massive structural under-utilization and bubble injection.

The TCP redefines generality through micro-architectural plasticity, deploying flexible execution units anchored by fluid dot-product primitives. By integrating these primitives directly inside the compute blocks, the hardware exposes multiple physical pathways for data flow. The compiler can dynamically configure these lanes to modify data-reuse vectors in real time, maintaining peak hardware utilization across diverse tensor layouts.

Chip design has long traded one thing for another: GPUs are general but spend energy on control and data movement; fixed-function arrays are efficient but rigid.

The Tensor Contraction Processor sits between the two: programmable enough for changing models and agentic workloads, and efficient because it runs tensor contraction natively. By mapping the higher-dimensional geometry of the tensor directly onto the silicon, the TCP unifies software-programmable dataflow generality with the deterministic power efficiency of dedicated primitives.