FuriosaAI software platform roadmap showcasing support for leading AI and LLM ecosystems, including OpenAI, Anthropic, Google, Meta, DeepSeek, Alibaba, and Mistral models, alongside continuous SDK advancements in inference optimization, parallelism, reasoning workloads, and scalable AI deployment.

Furiosa Software

Co-designed with the Tensor Contraction Processor

FuriosaAI’s full-stack AI software platform integrates PyTorch, Tensor Contraction Language, compiler optimization, binary generation, LLM serving infrastructure, and Kubernetes-based deployment to deliver efficient AI inference from model development to production-scale systems.

A unified serving, programmin, and deployment stack engineered to map complex neural networks directly to silicon. Build, optimize, and scale models with predictability across Furiosa hardware.

Radical efficiency across compilation and execution

Optimizations cover the whole inference lifecycle, from ahead-of-time compilation to an efficient serving runtime, so you fit more inference into the same hardware.

Dark sphere with evenly spaced small white dots forming a 3D grid around a black square center.

Global optimization via holistic search

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High-throughput, low-latency scheduling

The GPU bottleneck: Why hand-tuned kernels don’t scale

Conceptual graphic showing matrix multiplication optimization, highlighting the two key challenges of GPU performance tuning: memory allocation and instruction selection and scheduling. Colored indicators distinguish memory management tasks from execution scheduling requirements.

Diagram illustrating GPU architecture with multiple threads, registers, shared memory, cache layers, and HBM memory. Highlighted regions represent memory allocation and instruction scheduling across processing units, demonstrating the complexity of optimizing data movement and execution for AI workloads.

Screenshot of low-level GPU kernel source code used for AI computation, showing memory management, tensor operations, synchronization logic, and performance optimization routines required to execute machine learning workloads efficiently.

When hardware execution is dynamic, a compiler can’t predict the best path for a multi-dimensional tensor workload. Teams fall back on hand-tuned kernels for each model and shape, which is slow to build and hard to keep current as models change. This structural mismatch leaves hardware underutilized.

The tensor-native advantage: Global optimization on predictable hardware

Conceptual diagram showing FuriosaAI’s optimization framework for tensor operations. Matrix multiplication is optimized through two key dimensions: memory allocation using tensor shapes and instruction selection and scheduling using execution tactics, balancing data placement and compute efficiency.

Diagram of FuriosaAI’s Tensor Contraction Processor (TCP) architecture featuring distributed SRAM blocks, fetch units, contraction engines, vector engines, registers, and commit units. Arrows illustrate data reuse and dot-product execution across processing elements, highlighting efficient data movement and parallel computation.

Screenshot of FuriosaAI Tensor Contraction Language (TCL) code defining a matrix multiplication operation. The code demonstrates hardware-aware tensor mapping, memory allocation, data fetching, contraction operations, accumulation, reduction, and execution scheduling for optimized AI inference workloads.

We designed the compiler and the architecture together. Because hardware executions on the Tensor Contraction Processor are predictable, the compiler plans memory allocation and instruction scheduling ahead of time, searches a bounded space of shapes and tactics, and generates an optimized execution plan for the whole graph. New models run well without hand-written kernels.

TCP advantages: Accurate cost model for compiler

Visualization of FuriosaAI TCP’s deterministic execution model compared with traditional GPU architectures. The graphic highlights predictable low-latency AI inference, reduced performance variability, and consistent execution behavior that improves reliability for production-scale AI workloads.

AI architecture diagram showing tensor contraction as the primary computational workload in transformer models, linking tensor data structures, neural network layers, and weighted activation functions used in modern AI inference.

What matters is how a platform behaves under production load. The Furiosa compiler uses an accurate hardware cost model, so latency stays predictable and tail latency stays low, as the distribution shows. Infrastructure teams can pack more concurrent workloads into a fixed power budget and still meet their SLAs.

Drop-in GPU replacement. Migrate your entire client code today.

OpenAI compatible API endpoint showing install and serve commands plus example API request with curl.

Python code snippet loading a Hugging Face LLM model and sending a chat message asking France's capital.

Kubernetes and llm-d logos above 'Cloud-native device plugin' text and YAML pod configuration snippet.

Software compatibility across the modern AI stack, including modern LLM serving APIs, the vLLM-compatible interface, and seamless integration with existing cloud infrastructure.