Open-weight MoE

Trinity Large Preview (398B MoE)

Catalogue summary: Arcee AI's server-grade sparse MoE. The official model card lists about 398B total and 13B active parameters; the official Q4_K_M GGUF is about 241.6 GB. OpenMDW 1.1 licensed.

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

320 GB catalogue minimum320 GB RAMQ4_K_MCoding assistant
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Parameters
398B total (13B active, MoE)
Minimum RAM
320 GB
Model size
241.6 GB
Quantization
Q4_K_M

Can Trinity Large Preview (398B MoE) run locally?

Trinity Large Preview (398B MoE) has a catalogue minimum of 320 GB RAM with Q4_K_M. Actual memory use and speed vary by context length, runtime, backend and system headroom.

Treat trinity-large-preview as an advanced workstation/server-grade catalogue target with verified GGUF artefacts. Open the GGUF files and follow the upstream runtime notes; LocalClaw does not claim a one-click LM Studio install.

chatcodereasoningpowerqualitygeneralserver-grademoe

Deployment path

01
Check RAM fitServer-grade target. Plan for 320 GB class multi-GPU memory.
02
Load the modelOpen the verified GGUF files for trinity-large-preview and follow the upstream runtime or patch notes first.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Catalogue record

  • Family: trinity
  • Parameters: 398B total (13B active, MoE)
  • Recommended quantization: Q4_K_M
  • Catalogue minimum RAM: 320 GB
  • Catalogue model size: 241.6 GB
  • Tags: chat, code, reasoning, power, quality, general, server-grade, moe

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
  • power
  • quality
  • general

Capability profile

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

speed
3
quality
10
coding
10
reasoning
10

Technical notes

Developer
Arcee AI
License
OpenMDW 1.1
Context window
131,072 tokens
Architecture
Sparse Mixture of Experts, about 398B total and 13B active per token

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.

Related catalogue entries

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

Where to go next