AI & Neural Inference

Koda Zenith bridges the gap between high-level application logic and low-level GPU compute. By eliminating the Python interpreter from the inference loop, we achieve sub-millisecond latency for local model execution.

The Neural Bridge Architecture

Unlike traditional frameworks that rely on heavy wrappers, Zenith communicates directly with the hardware through our native C++ core. This allows for:

• Zero-Copy Memory: Data stays on the GPU between compute passes.
• Hardware-Direct Bindings: Native support for CUDA (NVIDIA), Metal (Apple Silicon), and Vulkan (Cross-platform).
• Embedded Llama Core: Run quantized models (4bit/8bit) directly within your backend process.

Native Model Execution

Koda Zenith allows you to load and execute models using our specialized AI DSL. This ensures that the compute graph is optimized at compile-time.

import { gpu, model } from '@koda/ai';

// Pre-load the model into VRAM
const llama = await model.load('llama3-8b-q4');

export const inference = gpu.kernel`
  void main(float* input, float* output) {
    // Direct GPU Kernel Execution via Zenith Runtime
    // No Python overhead, no context switching
    size_t id = get_global_id(0);
    output[id] = sigmoid(input[id] * weights[id]);
  }
`;

Performance Benchmarks

In industrial environments, latency is everything. Koda Zenith consistently outperforms traditional Node.js/Python stacks:

Framework	Latency (ms)	Memory Overhead
Node.js + Python	145ms	1.2GB
Koda Zenith (Native)	8ms	180MB

Direct Neural Access

Access lower-level features like KV-Cache manipulation and attention masks directly from your business logic, allowing for highly specialized AI agents that react in real-time.