Why SeaBiscuit exists

State‑of‑the‑art language and vision models can be larger than 100 GB once weights and activations are in memory. Most enterprise GPUs top out at 16 GB. The default fix is to rent cloud GPUs, but that introduces latency, egress fees, and data‑sovereignty headaches. SeaBiscuit keeps inference inside the firewall by chopping the model into pieces that fit the hardware you already have.

Hierarchical decomposition

Any neural network—whether built in PyTorch, exported to ONNX, or loaded from Hugging Face—can be represented as a computational graph. SeaBiscuit applies hierarchical decomposition to break dense layers into thousands of micro-operations. Instead of one massive matrix multiplication consuming 40GB, you get 2,500 tiny operations that can each fit in 16MB of memory.

The decomposition preserves mathematical equivalence while exposing parallelism that was previously hidden. Each micro-operation becomes an independent unit that can execute on any device with sufficient memory and computational capability. The system builds a detailed dependency graph mapping exactly which operations must complete before others can begin.

Hardware agnostic execution

SeaBiscuit treats every compute resource as a potential execution target. A single inference might use GPU cores for attention mechanisms, CPU cores for embedding lookups, and specialized accelerators for quantized operations. The system maintains performance profiles for every operation type across different hardware classes.

The placement engine understands hardware hierarchies—L1 cache sizes, memory bandwidth characteristics, instruction throughput rates. It knows that certain operations perform better on CPUs due to branching patterns, while others benefit from GPU parallelism. This heterogeneous execution extends to memory systems, utilizing both DDR and high-bandwidth memory optimally.

Dynamic model ingestion

When you load a model, SeaBiscuit's tracer walks through the computational graph, identifying layer boundaries and operation dependencies. For transformer models, it recognizes attention patterns and feed-forward structures. For convolutional networks, it maps kernel operations and pooling layers. The tracer works with any standard model format—PyTorch state dictionaries, ONNX graphs, or serialized checkpoint files.

The system then applies cost modeling to estimate memory requirements and computational intensity for each discovered operation. This analysis happens once per model architecture, creating a template that can be reused across different parameter sets or input sizes.

Distributed resource discovery

Your infrastructure becomes a single logical compute fabric. SeaBiscuit deploys lightweight agents across every server, continuously monitoring CPU utilization, GPU memory availability, and network bandwidth. These agents report not just current capacity, but predicted availability based on scheduled workloads and historical patterns.

The resource map includes detailed topology information—which GPUs share PCIe switches, which servers connect through the same network fabric, which memory banks offer the lowest latency access. This topology awareness enables the system to minimize data movement costs during placement decisions.

Adaptive orchestration

When inference begins, the placement engine solves a complex optimization problem: assign thousands of micro-operations to hundreds of potential locations while minimizing total execution time. The algorithm considers memory constraints, network bandwidth limits, and current workload patterns simultaneously.

The solution involves multi-level partitioning—first grouping operations into larger blocks that can execute independently, then mapping these blocks to specific hardware resources. The system uses graph-theoretic algorithms to find optimal cut points that minimize inter-block communication while respecting device memory limits.

Network-aware tensor routing

Data movement orchestration operates at multiple layers. Within servers, tensors flow through PCIe lanes, NVLink connections, and shared memory segments. Between servers, the system utilizes RDMA-capable networks, InfiniBand fabrics, or high-speed Ethernet with kernel bypass techniques.

The routing layer maintains thousands of concurrent data streams, each with adaptive flow control and congestion avoidance. When network conditions change, the system can reroute active transfers through alternate paths without disrupting ongoing computations. This requires maintaining distributed state consistency across potentially hundreds of participating nodes.

Real-time migration

The most complex capability involves live workload migration. When resource availability changes—a batch job completes, a GPU develops thermal issues, or network congestion appears—SeaBiscuit can relocate running operations to maintain performance targets.

Migration happens at the granularity of individual micro-operations. The system must serialize intermediate state, transfer it to new locations, and reconstruct execution context while maintaining mathematical correctness. This coordination requires distributed consensus protocols and precise timing synchronization across the entire compute fabric.

Fault tolerance depth

Distributed inference introduces failure modes absent in single-device execution. Network partitions can isolate operation subgraphs, memory corruption can affect tensor integrity, and device failures can eliminate entire execution branches. SeaBiscuit implements hierarchical checkpointing that operates at microsecond granularity.

The system maintains versioned state across all participating nodes, enabling rollback to consistent snapshots when failures occur. Recovery involves reconstructing the computational graph, redistributing affected operations, and resuming execution from the most recent valid checkpoint—all while preserving numerical accuracy.

Implementation complexity

Beneath the simple API lies a coordination system managing thousands of concurrent execution threads, distributed memory allocators tracking tensor lifecycles across NUMA domains, and network protocols optimized for specific interconnect hardware. The runtime maintains detailed performance models accounting for cache effects, memory bandwidth limitations, and thermal throttling behaviors.

Custom schedulers balance computational load while respecting complex dependency constraints. The system uses machine learning to predict resource demands and optimize placement decisions based on historical execution patterns. This meta-learning operates continuously, adapting to changing workload characteristics and infrastructure conditions.

Current prototype scale

• Model ingestion: Any PyTorch/ONNX architecture with automatic decomposition
• Hardware diversity: CPUs, GPUs, and mixed heterogeneous deployments
• Graph complexity: 2,500+ micro-operations across 40+ GB model weights
• Device coordination: 10+ processors across multiple servers and rack configurations
• Response time: <25 ms replanning when resource availability shifts

Research frontiers

The fundamental challenge involves balancing execution efficiency with coordination overhead as system scale increases. Current work focuses on hierarchical scheduling algorithms that reduce communication complexity, and predictive placement techniques that anticipate resource demands before they materialize.

Advanced quantization methods could enable more aggressive decomposition strategies, while emerging memory-semantic interconnects promise new opportunities for distributed state management and ultra-low-latency tensor routing.