Hey everyone,

Over the past few months, I’ve been working on Ghostlink, an open-source distributed inference fabric designed to harness whatever mixed hardware you have lying around on your local network.

Instead of assuming a homogeneous fleet of high-end GPUs, Ghostlink pools together heterogeneous machines (like a gaming rig, an older laptop, an NPU-equipped ultrabook, or a Mac) into a single unified inference cluster—without requiring complex YAML setups or manual RPC flags.

It’s completely open source under the MIT license. Check out the Ghostlink GitHub repository to view the benchmarks, architecture breakdown, or run it locally.

I’d love to get feedback from the community on hardware setups or features you'd like to see next!

Here is the Automated report.

Ghost-Link just finished its latest automated full-spectrum stress runs, and the async performance numbers are looking elite. By utilizing zero-copy memory mapping and a highly optimized asynchronous scheduler, it completely bypasses the standard network serialization taxes that plague typical enterprise distribution layers.
Performance vs. Enterprise Tiers
In-Memory (64 tokens, batch 8): 210,200 t/s (+133% vs Industry Avg)
In-Memory Peak (2048 tokens, batch 256): 673,396 t/s (+140% vs Industry Avg)
TCP Network (256 tokens, batch 32): 166,447 t/s (+269% vs Industry Avg)
TCP Network Peak (2048 tokens, batch 256): 353,255 t/s (+260% vs Industry Avg)
Why It Wins
TCP Stack Efficiency: Most commercial software drops down to 20–30% of raw in-memory speed over the network. Ghost-Link retains 44% to 52% of its raw inmem speed over TCP by eliminating user-to-kernel context switching.
Parallel Scaling: Zero lock contention. Instead of hitting a resource wall under heavy loads, throughput actually accelerates as data density increases.
Chaos-Proof Jitter: During simulated chaos injection, the performance delta remained negligible (averaging a stable 551,313 t/s under inmem-512 disruption). The async scheduler handles massive packet bursts without cascading tail-latency spikes.
Generated automatically from the Full Spectrum Benchmark run (July 19, 2026).
https://github.com/rwilliamspbg-ops/Ghostlink
#RustLang #SystemsEngineering #BackendPerformance #HighThroughput #Concurrency

I have been playing around recently with Rust, this is a Distributed LLM inference fabric for heterogeneous local clusters. Features zero-copy SPSC ring buffers, automated tuning, and an OpenAI-compatible API. I invite anyone interested into contributing your ideas and incite.

https://github.com/rwilliamspbg-ops/Ghostlink

link: https://github.com/rwilliamspbg-ops/Mohawk-Nexus

we built an open-source networking layer designed to execute strict, machine-checked verification invariants directly inside an ultra-low-latency data path. the project bypasses the linux kernel networking stack completely via custom AF_XDP driver bindings to maintain zero-copy efficiency.

the main problem we're solving is the massive performance penalty that usually comes with running complex cryptographic or mathematical validation inside a fast pipeline. right now, the repo contains the low-level rust and go scaffolding for the ring buffers, the UMEM frame manager, and a proof-of-concept validation loop.

core tech stack:

• AF_XDP / eBPF for direct driver-level packet ring access
• Lean 4 compiled invariants mapped to runtime user-space execution
• Zero-copy memory architecture

current status: the scaffolding hits line rate without validation, but we are actively debugging L1/L2 cache thrashing inside the ring processing loop when processing heavy weight matrices for heterogenous node states. the code is fully open source, and we're looking for feedback specifically from anyone who has wrangled memory boundary alignment or ring batching mechanics in custom kernel-bypass drivers.

🎧 Want to get under the hood? We break down the engineering reality of decentralized systems and zero-trust networks on the Sovereign Mohawk: The Future of Private Infrastructure daily podcast.

👉 Tune in for today's episode on Spotify and join the deep dive.

https://www.deepsurge.xyz/projects/e9d19c4b-e3d5-41b6-b986-656e74fec628

I have been working on a Formal Network Stack proven by Lean4. Anyone out there able to get some metrics on different machines for me? https://github.com/rwilliamspbg-ops/Mohawk-Nexus

I’m developing an open-source protocol, Sovereign-Mohawk-Proto, focused on sovereign-grade federated learning. I am looking for researchers and engineers to help refine the mathematical and security foundations.

The Research Focus:

• Security: Implementing Post-Quantum Cryptography (PQC) and hardware-rooted security (TPM 2.0).
• Aggregation: Utilizing Multi-Krum algorithms for Byzantine Fault Tolerance in modular compute environments.
• Architecture: Built on the Sovereign SDK for modular rollup logic.

If you are in academia or an engineer working on verifiable compute and privacy-preserving ML, I’d love your feedback or collaboration on the protocol's formal verification.