GPU-accelerated proving infrastructure optimized for sustained throughput and predictable execution under continuous load. Built for production zkVM and SNARK systems.
Talaus operates dedicated, GPU-accelerated proving infrastructure optimized for sustained zero-knowledge proof generation.
Systems are provisioned with a focus on memory bandwidth, GPU residency, and stable kernel execution rather than burst throughput. Hardware is selected and configured to support large FFTs, MSM-heavy workloads, and recursive proving pipelines common in modern zk systems. Capacity is intentionally managed to avoid contention and ensure predictable execution under continuous load.
The infrastructure is designed to support a range of SNARK systems and zkVM architectures used in production environments.
This includes PLONK-family systems and their derivatives (e.g. Plonky-style constructions), Groth16-based circuits, and recursive proving frameworks that place distinct demands on memory, concurrency, and prover orchestration. Workloads are isolated to account for differing circuit sizes, proving phases, and resource profiles as additional zkVMs and proving backends are introduced.
Proof workloads are executed through a controlled scheduling model with explicit limits on concurrency, preflight execution, and proof generation.
The system is tuned for deterministic completion rather than maximum theoretical throughput. Advertised performance is set conservatively relative to observed benchmarks to account for real-world variance, queueing effects, and prover phase imbalance. Timeout handling, retries, and failure modes are managed explicitly to minimize job loss and maintain consistent delivery behavior.
Infrastructure is continuously monitored for GPU utilization, memory pressure, execution latency, and job outcomes across proving phases.
These signals are used to tune concurrency limits, throughput assumptions, and bidding behavior over time. Changes to execution parameters are made incrementally and validated against production behavior, with priority placed on low timeout rates and stable fulfillment rather than aggressive capacity advertising.
Talaus engages with the ZK ecosystem as both an infrastructure operator and a technical participant.
Contributions are made selectively where they improve operational robustness, platform accessibility, or prover deployment flexibility across architectures and environments. This work is shared publicly where appropriate, with the goal of strengthening the broader proving ecosystem rather than building proprietary lock-in.
Production-ready ARM64/aarch64 support for the SP1 Cluster proving infrastructure stack, enabling deployment on ARM-based systems including NVIDIA DGX Spark.
Includes ARM64-specific Dockerfiles, automated build scripts, CUDA configuration for GPU nodes, and operational documentation. Reduces deployment friction for operators running SP1 Cluster on ARM hardware by providing pre-configured build pipelines and runtime images.
The implementation maintains full compatibility with existing x86_64 deployments while adding native ARM64 support, ensuring seamless integration with existing SP1 Cluster workflows and infrastructure management tools.
Automated build pipeline and comprehensive operator documentation for ARM64 deployments.
Single-command build script handles CPU and GPU binary compilation, Docker image generation, and dependency verification. Includes troubleshooting guides, CUDA configuration notes, and performance optimization recommendations for ARM64 systems.
Build automation supports both CPU-only and GPU-enabled deployments, with automatic detection of CUDA installations and proper library path configuration for ARM-specific CUDA layouts.