| --- |
| base_model: google/gemma-4-8b-it |
| library_name: nexus-core |
| license: apache-2.0 |
| pipeline_tag: text-generation |
| tags: [rust, bare-metal, edge-ai, mcp, zero-copy, gguf] |
| --- |
| |
| # Nexus-Core × Gemma 4 (8B-IT) |
|
|
| ## 🚀 Enterprise Integration & Strategic Acquisition |
|
|
| Nexus-Core represents a paradigm shift in bare-metal Edge AI orchestration. By collapsing the Python GIL bottleneck through a zero-copy Rust core, eliminating PagedAttention VRAM fragmentation via reference-counted Copy-on-Write KV blocks, and enforcing deterministic semantic routing through a Zero-Trust MCP gatekeeper, the architecture delivers production-grade reliability where conventional Python-first stacks degrade under load. The full intellectual property — covering the Codata substrate, the continuous-batching scheduler, the lock-free hardware profiler, and the cognitive reliability layer — is available for **B2B licensing, enterprise deployment partnerships, or strategic acquisition**. |
|
|
| - **GitHub Repository:** [rupertin123/nexus-core](https://github.com/rupertin123/nexus-core) |
| - **Lead Architect:** Lucas Aloisio |
| - **Email for Inquiries:** [lucasaloisio6@gmail.com](mailto:lucasaloisio6@gmail.com) |
| - **LinkedIn:** [Connect on LinkedIn](https://www.linkedin.com/in/lucas-aloisio-a17608240/?skipRedirect=true) |
|
|
| Nexus-Core wraps `google/gemma-4-8b-it` in a deterministic, Rust-backed |
| orchestrator designed for the Edge: PagedAttention with Copy-on-Write |
| prefix sharing, a Zero-Trust MCP gatekeeper, lock-free hardware |
| telemetry, and a continuous-batching scheduler that survives |
| oversubscribed workloads without OOM. The repository ships |
| pre-compiled wheels for Linux (x86_64 / aarch64), macOS (x86_64 / |
| aarch64), and Windows (x86_64); end users never touch a Rust toolchain. |
| |
| ## Recommended GGUF Quantizations for Nexus-Core |
| |
| | Quantization | Use Case | VRAM (PagedAttention Est.) | Target Hardware | |
| | ------------ | -------- | -------------------------- | --------------- | |
| | `Q4_K_M` | Balanced laptop / on-device assistant; best size-quality trade-off for interactive agents. | ~5.5 GB at 32k ctx, ~7 GB at 128k ctx with CoW prefix sharing. | Apple M-series (8–16 GB unified memory), NVIDIA RTX 4060 / 4070 mobile, ROCm 7900M. | |
| | `Q8_0` | Server-side accuracy; near-FP16 fidelity for evaluation, distillation, or compliance-grade inference. | ~9 GB at 32k ctx, ~11 GB at 128k ctx. | NVIDIA RTX 4090 / 5090, A100 40 GB, H100 PCIe slice. | |
| | `AWQ` | Pure GPU throughput; activation-aware 4-bit weights for high-QPS deployments behind the continuous-batching scheduler. | ~6 GB at 32k ctx with batched KV-cache reuse. | NVIDIA L4 / L40S, RTX 5080, Jetson AGX Orin 64 GB. | |
|
|
| ## Contact & Community |
|
|
| Architectural feedback, open-source collaboration, and B2B / VC inquiries are all welcome. The fastest way to start a conversation is a direct message on either of the channels below. |
|
|
| - **Email:** [lucasaloisio6@gmail.com](mailto:lucasaloisio6@gmail.com) |
| - **LinkedIn:** [Lucas Aloisio](https://www.linkedin.com/in/lucas-aloisio-a17608240/) |
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|
