LocalClaw/LLM/DeepSeek R1 0528 (671B MoE)
Open-weight local LLM

DeepSeek R1 0528 (671B MoE)

Updated flagship DeepSeek R1 with improved reasoning chains and fewer hallucinations. Major upgrade to chain-of-thought quality. MIT licensed. Server-grade only.

Server-grade 512 GB RAM Q4_K_M Server-grade reasoning deployment
Run with LocalClaw Compare all models
Parameters
671B (37B active)
Minimum RAM
512 GB
Model size
360 GB
Quantization
Q4_K_M

Can DeepSeek R1 0528 (671B MoE) run locally?

DeepSeek R1 0528 (671B MoE) is server-grade locally. Keep it for comparison unless you have very large unified memory, multiple GPUs or remote inference.

Search for deepseek-r1-0528 in LM Studio or another GGUF-compatible runtime.

deepseek-ai/DeepSeek-R1-0528-GGUF
reasoningcodequality

Install path

01
Check RAM fitMinimum 512 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch deepseek-r1-0528 in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Strengths

  • Significantly improved reasoning chains vs original R1
  • Fewer hallucinations and better self-correction
  • MIT license — fully open
  • 128K context
  • State-of-the-art on math and coding benchmarks

Limitations

  • Requires 512GB+ RAM (server-grade only)
  • Slower than non-reasoning models
  • Still verbose in reasoning chains

Best use cases

  • Server-grade reasoning deployment
  • Mathematical research
  • Complex coding tasks
  • Scientific analysis
  • Enterprise on-premise AI

Capability profile

speed
1
quality
10
coding
10
reasoning
10

Technical notes

Developer
DeepSeek AI
License
MIT
Context window
131,072 tokens
Architecture
Mixture of Experts (MoE) — 671B total, 37B active per token. Updated from original R1.

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

Similar models to compare

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Where to go next