DeepSeek-R1-0528-NVFP4-v2 Locally via LM Studio For Low VRAM (6GB/8GB) Offline Setup

by | Jun 28, 2026 | WebUIs | 0 comments

DeepSeek-R1-0528-NVFP4-v2 Locally via LM Studio For Low VRAM (6GB/8GB) Offline Setup

Running this model locally is fastest when deployed through Docker.

Follow the guidelines below to continue.

Finally, execute the Docker command to bring the container online.

🧾 Hash-sum — d3a34f24835d365de71692662b0864f5 • 🗓 Updated on: 2026-06-27



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space: required: fast PCIe 4.0 drive for instant boots
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

DeepSeek-R1-0528-NVFP4-v2 is a large language model optimized for low‑precision inference on NVIDIA’s Hopper architecture. It leverages NVFP4 data type to achieve higher throughput while maintaining state‑of‑the‑art accuracy. The model features a parameter count of 180 B and was trained on over 5 trillion tokens, enabling robust reasoning across diverse domains. Its inference latency averages 23 ms per token on a single A100‑80GB, making it suitable for real‑time applications. The design incorporates mixture‑of‑experts layers that dynamically route queries to specialized subnetworks, improving both efficiency and scalability. Below is a quick comparison of key technical specifications:

Parameter Count180 B
Training Tokens5 trillion
Inference Latency23 ms/token
PrecisionNVFP4
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