Install deepseek-v4-gguf on AMD/Nvidia GPU with 1M Context Direct EXE Setup

Install deepseek-v4-gguf on AMD/Nvidia GPU with 1M Context Direct EXE Setup

Running this model locally is fastest when deployed through a PowerShell script.

Kindly follow the on-screen instructions below.

The script takes care of fetching the multi-gigabyte model weights.

Once launched, the wizard detects your specs to configure the model for maximum efficiency.

🔒 Hash checksum: aeb2457b57c98664f08f57ebfa95af37 • 📆 Last updated: 2026-06-30



  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: 32 GB highly recommended for 26B+ GGUF models
  • Disk Space: required: fast PCIe 4.0 drive for instant boots
  • Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

The deepseek-v4-gguf model represents a significant advancement in open‑source language models, combining efficient quantization with state‑of‑the‑art performance. Built on a transformer‑based architecture, it leverages grouped‑query attention to reduce memory footprint while maintaining high inference speed on consumer hardware. With 7 billion parameters and a 8 K context window, the model excels at both reasoning tasks and creative generation, delivering competitive scores on benchmark suites. The GGUF format ensures compatibility across multiple platforms, allowing developers to integrate the model seamlessly into existing pipelines without extensive optimization. A comparison table below highlights key specifications and performance metrics relative to earlier deepseek releases.

Parameter Count 7 B
Context Length 8 K tokens
Quantization GGUF
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