LocalClaw/LLM/DeepSeek V3.2 Exp (671B MoE)
Open-weight local LLM

DeepSeek V3.2 Exp (671B MoE)

Experimental V3.2 with DeepSeek Sparse Attention (DSA) — halves inference cost vs V3.1 on long context while keeping quality. 128K context, improved coding & tool-use. MIT licensed. Server-grade.

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

Can DeepSeek V3.2 Exp (671B MoE) run locally?

DeepSeek V3.2 Exp (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-v3.2-exp in LM Studio or another GGUF-compatible runtime.

deepseek-ai/DeepSeek-V3.2-Exp
chatcodereasoningquality

Install path

01
Check RAM fitMinimum 512 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch deepseek-v3.2-exp in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Strengths

  • Experimental V3.2 with DeepSeek Sparse Attention (DSA) — halves inference cost vs V3.1 on long context while keeping quality. 128K context, improved coding & tool-use. MIT licensed. Server-grade.

Limitations

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

Best use cases

  • chat
  • code
  • reasoning
  • quality

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