Llama-3.1-8B-Instruct-OpenVINO-INT8 (Platinum Series)

This repository contains the Platinum Series high-precision OpenVINO INT8 release of Llama-3.1-8B-Instruct. This collection represents a monumental leap in local AI, featuring a 128k context window and state-of-the-art reasoning capabilities. Engineered for users who require zero-compromise reasoning accuracy while utilizing Intel’s optimized inference acceleration.

📦 Available Files & Quantization Details

File Name	Quantization	Accuracy	Recommended For
openvino_model.bin	INT8_ASYM	100% Fidelity	Enterprise Finance / Legal
Compression Mode	Per-Channel	Maximum Range	Accuracy-Critical Tasks

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🐍 Python Inference (OpenVINO GenAI)

To run this engine using Python:

from openvino_genai import LLMPipeline

# Load the high-fidelity Platinum INT8 engine
pipe = LLMPipeline("Llama-3.1-8B-Instruct-OpenVINO-INT8", "CPU")

# Processing complex MCA notifications
print(pipe.generate("List compliance requirements for Section 203 of the Companies Act.", max_new_tokens=1024))

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💻 C# / .NET Users (OpenVINO.GenAI.CSharp)

This collection is fully compatible with .NET applications via the OpenVINO.GenAI C# API, ideal for integrating into corporate tools.

using OpenVino.GenAI;

// Load the high-fidelity Platinum INT8 engine
var pipeline = new LLMPipeline("Llama-3.1-8B-Instruct-OpenVINO-INT8", "CPU");

// Process complex MCA notifications
var result = pipeline.Generate("Summarize the 2026 MCA updates for listed companies.", max_new_tokens: 1024);
Console.WriteLine(result);

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🏗️ Technical Forge

• Optimization Tool: optimum-cli / NNCF (2026-03-29)
• Bitwidth Distribution: 100% INT8_ASYM (Full Weight Compression)
• Workstation: Dual-GPU (NVIDIA RTX 3090 24GB + RTX A4000 16GB)
• Infrastructure: S: NVMe Scratch / K: 12TB Warehouse

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☕ Support the Forge

Maintaining the production line for high-fidelity models requires significant hardware resources. If these tools power your research or industrial projects, please consider supporting the development:

Platform	Support Link
Global & India	Support via Razorpay

Scan to support via UPI (India Only):

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Connect with the architect: Abhishek Jaiswal on LinkedIn