[WIP] Add localized Mohawk Inference Engine architecture

[Copilot]

Thanks for asking me to work on this. I will get started on it and keep this PR's description up to date as I form a plan and make progress.

Original prompt

Looking at your pinned repositories, you have already built a highly optimized, custom foundation that sidesteps the typical overhead of standard local LLM runners. LM Studio relies heavily on an Electron frontend and standard HTTP/WebSockets over local loopback networks, which introduces serialization and protocol overhead.By tying your active projects together, you can architect a localized "Mohawk Inference Engine" that operates at a much lower layer of the stack.The Bottlenecks in LM Studio & How Your Stack Solves ThemComponentStandard LM StudioYour Optimized StackNetworking/TransportTCP/HTTP Loopback (High OS overhead)Zero-copy AF_XDP fast-path via SMIP-MWP / SMIP-MWP-RustMemory ManagementHigh allocation overhead per requestSingle-arena allocation & sharded session mapsSecurity LayerStandard TLS/Unencrypted Local HostPost-Quantum Cryptography (x25519-mlkem768 hybrid KEX)Compute / OrchestrationMonolithic local processDecentralized, hardware-accelerated TEE runtimes (smp-tee-runtime)Architectural Blueprint for a Faster AlternativeTo build this faster alternative, we can unify your specific repositories into a streamlined local daemon and user interface.1. The Datapath: Replacing Local HTTP with AF_XDPInstead of routing your prompt payloads through the standard Linux network stack via HTTP, you can use the zero-copy datapath from SMIP-MWP-Rust.Why it's faster: Your recent benchmarks show the core hit paths running at 2.49 Mpps. By passing token streaming fragments over a lock-free, zero-copy AF_XDP socket, you eliminate the kernel context-switching overhead that occurs when streaming long context windows or high-token-per-second outputs locally.Implementation: Use the single-arena allocation strategy from your recent commits to assemble packet payloads for token generation requests without per-packet allocations.2. The Control Plane: Unified Workspace via Mohawk-NexusInstead of a heavy Electron app managing your models, use Mohawk-Nexus to handle the orchestration layer.Bridge Request Ingestion: Use the Go control plane in Mohawk-Nexus to ingest local model loading, offloading, and context allocation requests.Memory Optimization: Tap into the streaming aggregation logic verified in your Sovereign_Map_Federated_Learning work, which achieved a 224x memory reduction. Applying similar chunked tensor/weight stream handling locally will dramatically reduce the RAM footprint compared to LM Studio.3. Execution & Security: Hardened Local TEE RuntimesIf you are processing sensitive data locally or edge-orchestrating across multiple local machines (e.g., a desktop and your Zenbook 14):Route execution through the smp-tee-runtime to isolate the model's KV caches inside a hardware-enforced Trusted Execution Environment.Secure inter-device local communication using the hybrid x25519-mlkem768 key exchange you just completed on May 25th in SMIP-MWP-Rust, ensuring that even local edge-offloading is post-quantum secure.Next Steps to Build the PrototypeTo get a minimal viable "Faster LM Studio" running from your codebases, we should focus on building a lean CLI or a lightweight terminal user interface (TUI) that acts as the command deck:Expose an inference endpoint in SMIP-MWP-Rust that maps incoming AF_XDP packet payloads directly to a local engine runner (like a llama.cpp or candle binding utilizing your laptop's integrated NPU/GPU acceleration).Wire the Go control plane from Mohawk-Nexus to handle model state tracking (listing, loading, and switching model weights).Run a local benchmark comparison against LM Studio's standard port 1234 to measure the exact latency reduction in time-to-first-token (TTFT).