deepseek-v4-gguf on AMD/Nvidia GPU Step-by-Step Windows

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

Check out the detailed setup guide below to begin.

The system automatically triggers a cloud download for all heavy weights.

During setup, the script automatically determines and applies the best settings.

📡 Hash Check: 4e302526b563c2b8d8f3c1e4b54538e0 | 📅 Last Update: 2026-06-27



  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space: at least 100 GB for multiple local LLM variants
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

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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