LocalClaw/LLM/DeepSeek V4 Pro (1.6T MoE)
Open-weight local LLM

DeepSeek V4 Pro (1.6T MoE)

DeepSeek frontier MoE with 1M-token context, hybrid compressed attention and top-tier coding/reasoning. MIT licensed. Datacenter-grade only.

Server-grade 1024 GB RAM FP4/FP8 Coding assistant
Run with LocalClaw Compare all models
Parameters
1.6T (49B active)
Minimum RAM
1024 GB
Model size
850 GB
Quantization
FP4/FP8

Can DeepSeek V4 Pro (1.6T MoE) run locally?

DeepSeek V4 Pro (1.6T MoE) is server-grade locally. Keep it for comparison unless you have very large unified memory, multiple GPUs or remote inference.

Search for deepseek-v4-pro in LM Studio or another GGUF-compatible runtime.

deepseek-ai/DeepSeek-V4-Pro
chatcodereasoningqualityagenticlong-contextgeneral

Install path

01
Check RAM fitMinimum 1024 GB RAM. Start with the FP4/FP8 quant.
02
Load the modelSearch deepseek-v4-pro in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Strengths

  • DeepSeek frontier MoE with 1M-token context, hybrid compressed attention and top-tier coding/reasoning. MIT licensed. Datacenter-grade only.

Limitations

  • Performance depends on quantization, RAM bandwidth and runtime support.

Best use cases

  • chat
  • code
  • reasoning
  • quality
  • agentic
  • long-context

Capability profile

speed
2
quality
10
coding
10
reasoning
10

Technical notes

Developer
deepseek
License
See model repository
Context window
Unknown tokens
Architecture
See model card

This model fits these next steps

Hardware fit is based on LocalClaw's RAM tier, model size and quantization metadata. Always leave memory headroom for your OS and runtime.

Very large memoryMac Studio Ultra classCheck model size firstNVIDIA GB10 / server optionsMore practical alternativesCompare smaller models

Where to go next