LocalClaw/LLM/DeepSeek V4 Flash 0731 (284B MoE)
Open-weight local LLM

DeepSeek V4 Flash 0731 (284B MoE)

Catalogue summary: Official MIT DeepSeek V4 Flash successor release with stronger agentic coding, DSpark speculative decoding support and a practical Unsloth Dynamic GGUF path. Still a large workstation/server local model: Q4 is about 155GB and Q8 is about 162GB.

Repository editorial metadata; verify comparative claims in the linked upstream material.

256 GB catalogue minimum256 GB RAMUD-Q4_K_XLCoding assistant
Choose an app

Only verified options can be opened. Unsupported apps are clearly marked.

Compare models
LM StudioNot available for this model
Open in UnslothLaunches Unsloth Desktop on this model's download page
OllamaNot available for this model
Open on Hugging FaceFiles, licence and available downloads
llama.cppNot available for this model
Use with LocalClawOptional workspace after the model is installed

Desktop app links require the app to be installed. If nothing opens, LocalClaw will show app-download and model-file fallbacks.

Parameters
284B (13B active)
Minimum RAM
256 GB
Model size
155 GB
Quantization
UD-Q4_K_XL

Can DeepSeek V4 Flash 0731 (284B MoE) run locally?

DeepSeek V4 Flash 0731 (284B MoE) has a catalogue minimum of 256 GB RAM with UD-Q4_K_XL. Actual memory use and speed vary by context length, runtime, backend and system headroom.

Treat deepseek-v4-flash-0731 as a server-grade catalogue target. Use the verified artefact with a compatible multi-GPU or distributed runtime and follow its upstream instructions; this is not a one-click desktop LM Studio recommendation.

Public GGUF repository (verified 2026-09-07): unsloth/DeepSeek-V4-Flash-0731-GGUFUpstream releasePaper or model cardLicence text
chatcodereasoningqualityagenticlong-contextgeneral

Deployment path

01
Check RAM fitServer-grade target. Plan for 256 GB class multi-GPU memory.
02
Load the modelUse deepseek-v4-flash-0731 only with a compatible server-grade or distributed runtime; confirm the exact artefact and upstream instructions first.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Catalogue record

  • Family: deepseek-flash
  • Parameters: 284B (13B active)
  • Recommended quantization: UD-Q4_K_XL
  • Catalogue minimum RAM: 256 GB
  • Catalogue model size: 155 GB
  • Tags: chat, code, reasoning, quality, agentic, long-context, general

Practical limits

  • Catalogue RAM is a minimum estimate, not a guarantee for every context length or runtime.
  • Speed and memory use vary by quantization, backend, context length and system headroom.
  • Verify architecture, licence and usage restrictions in the linked upstream material before deployment.

Catalogue tags

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

Capability profile

Repository catalogue ratings used by LocalClaw's editorial rubric. They are not a standardized third-party benchmark.

speed
5
quality
10
coding
10
reasoning
10

Technical notes

Developer
DeepSeek AI
License
MIT
Context window
1,048,576 tokens
Architecture
DeepSeek V4 Flash MoE successor release with 284B total parameters, about 13B active parameters, million-token context and the same DSpark speculative-decoding structure as DeepSeek V4 Flash DSpark.

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

Related catalogue entries

Linked mechanically by family, shared tags and nearby RAM tier; this is not a quality ranking.

DeepSeek V4 Flash (284B MoE) 284B (13B active)DeepSeek V4 Flash Vision Exp 284B (13B active, multimodal MoE)GLM-5.2 (744B MoE) 744B (40B active)GLM 4.7 355B (32B active, MoE)Qwen 3.5 MoE (397B/17B active) 397B (17B active)GLM 4.6 (355B MoE) 355B (32B active)Trinity Large Preview (398B MoE) 398B total (13B active, MoE)GLM-5.3-Flash 320B (18B active, MoE)

Where to go next