Arcee Trinity Large Thinking 400B
Trinity · Apache 2.0
Mixture of Experts: 399B total parameters, 13B active per token.
Arcee AI's largest Mixture-of-Experts reasoning model. 399B total parameters with only 13B active per token (256 experts, 4 selected; 1.56% routing, one of the sparsest MoE architectures in production). 512K native context. Trained on 17 trillion tokens across 2,048 B300 GPUs. Post-trained with extended chain-of-thought reasoning and agentic reinforcement learning. Ranks #2 on PinchBench behind Claude Opus-4.6. Requires a GB300 (288 GB) for full in-VRAM inference; even the M4 Ultra 192 GB cannot fit Q3_K_M. US-built, Apache 2.0 licensed.
- Parameters
- 399B
- Architecture
- MoE (13B active)
- Context
- 524,288 tokens
- Released
- 2026-04-01
- Engines
- llama.cpp, vLLM, SGLang
- Builder Tools
- Claude Code, Codex CLI, Continue, Cursor, LM Studio, Windsurf
Parameters
399B
VRAM
295 GB
Context
512K
Formats
3
GPUs
3
Arcee Trinity Large Thinking 400B (399B) requires 295 GB VRAM at recommended quality (Q5_K_M). On NVIDIA Grace Blackwell Ultra GB300, expect approximately 41 tok/s at Q4_K_M.
Source: OwnRig methodology
295 GB
Q5_K_M
285 GB
512K tokens
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VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| recommended | Q5_K_M | 295 GB | 285 GB |
| efficient | Q4_K_M | 255 GB | 244 GB |
| compressed | Q3_K_M | 195 GB | 185 GB |
Context Length Impact
KV cache VRAM at Q5_K_M quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 410 MB | 295.4 GBexceeds 24 GB |
| 4K | 819 MB | 295.8 GBexceeds 24 GB |
| 8K | 1.6 GB | 296.6 GBexceeds 24 GB |
| 16K | 3.2 GB | 298.2 GBexceeds 24 GB |
| 32K | 6.4 GB | 301.4 GBexceeds 24 GB |
| 64K | 12.8 GB | 307.8 GBexceeds 24 GB |
| 128K | 25.6 GB | 320.6 GBexceeds 24 GB |
Compatible GPUs
3 devices| NVIDIA Grace Blackwell Ultra GB300 | Q4_K_M | 41 tok/s | Excellent |
| Apple M4 Max (128GB Unified) | Q3_K_M | 1 tok/s | Not viable |
| Apple M4 Ultra (192GB) | Q3_K_M | 3 tok/s | Not viable |
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Builder Context
Arcee Trinity Large Thinking 400B is commonly used with Claude Code, Codex CLI, Continue, Cursor, LM Studio, Windsurf. For an AI coding workflow, pair it with an embedding model like nomic-embed-text for local RAG.
Frequently Asked Questions
- How much VRAM does Arcee Trinity Large Thinking 400B need?
- Arcee Trinity Large Thinking 400B requires 295 GB VRAM at recommended quality (Q5_K_M). At lower quality settings, it can fit in as little as 195 GB.
- What is the best GPU for Arcee Trinity Large Thinking 400B?
- The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Arcee Trinity Large Thinking 400B, achieving 41 tok/s at Q4_K_M with an excellent rating.
- What quantization should I use for Arcee Trinity Large Thinking 400B?
- For the best quality, use Q5_K_M (295 GB VRAM). If your GPU has limited VRAM, Q3_K_M (195 GB) is the most efficient option with acceptable quality.
- Is Arcee Trinity Large Thinking 400B good for coding?
- Yes. Arcee Trinity Large Thinking 400B is used with Claude Code, Codex CLI, Continue, Cursor, LM Studio, Windsurf for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.
Data confidence: estimated. Source
VRAM requirements are calculated from model parameters and may vary by inference engine, context length, and batch size. Performance estimates are based on community benchmarks and should be verified for your specific configuration.Trinity is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.