Technical architecture

See how Titan thinks about compute.

Titan plans across the complete path from user intent to models, nodes, accelerators, network, storage, context and power.

User / Taskintent, urgency, privacy, latency target
Capability Routerdirect tool, worker, general, reasoning, coding, vision or specialist
Context Fabricdurable memory, project context, retrieval, caching and boundaries
Adaptive Compute Plannermodel, quantization, replica/shard, node, power and network path
Inference EngineKV, batching, prefill/decode, speculative decoding and parallelism
Titan Fabricheterogeneous nodes across Windows, Linux, Apple Silicon and compatible systems
Network / StorageRDMA-aware transport, TCP fallback, NVMe, NAS, topology and telemetry
Inference capabilities

Deep runtime features.

Advanced users can understand exactly what Titan can optimize, while normal users can leave the decisions automatic.

Prefill / Decode Disaggregation

Separate prompt ingestion from token generation when measured KV-transfer and transport cost makes it worthwhile.

KV Transfer

Move reusable model state between compatible execution stages with topology and latency awareness.

Paged KV Cache

Manage attention memory efficiently across concurrent requests and long contexts.

Continuous Batching

Dynamically combine active requests to improve accelerator utilization.

Chunked Prefill

Reduce long-prompt interference by breaking prefill into scheduler-friendly work units.

Prefix Caching

Reuse safe repeated prefixes while respecting privacy and tenant isolation.

Speculative Decoding

Use draft/verification strategies where compatible models and hardware improve real output speed.

Tensor Parallelism

Split tensor operations across accelerators when interconnect bandwidth supports it.

Pipeline Parallelism

Partition model stages across devices or nodes for larger models and throughput.

Expert Parallelism

Place MoE experts according to access patterns, memory and network topology.

Context Parallelism

Distribute long-context work when the communication trade-off is favorable.

Elastic Model Streaming

Extend usable model size through accelerator ↔ RAM ↔ NVMe movement with explicit performance expectations.

Network-aware

Bandwidth, latency, jitter, packet loss, MTU, NIC capability and congestion can influence placement.

Storage-aware

Model files, context, checkpoints and caches can be placed across local NVMe and shared storage.

Power-aware

UPS state, thermals, energy constraints and user activity can lower priority or gracefully shed workloads.

Self-healing

Health checks, diagnostics, safe recovery, rollback and future fleet repair keep the system resilient.