Frank
02883d3ed2
Copy of the legacy ~/.pi/agent/docs tree rebuilt as the command-center knowledge base: concept files promoted to path identity, index listings stripped of frontmatter, all wikilinks/relative links resolved, every checkable claim verified against the live fleet (Dankserver = Ryzen 7 7700X + RTX 3090; Strix Halo is Dankclaw), unverifiable claims tagged. kb-lint exits 0.
1.9 KiB
1.9 KiB
type, title, description, tags, verified
| type | title | description | tags | verified | |||||
|---|---|---|---|---|---|---|---|---|---|
| reference | Ornith Model Specifications | Specifications for Ornith-1.0-9B dense and Ornith-1.0-35B MoE models — context length, quantization options, memory requirements. |
|
2026-08-28 |
Model Specifications
Ornith-1.0-9B-GGUF
- Source: https://huggingface.co/ornith-ai/Ornith-1.0-9B-GGUF
- Architecture: Dense ~9B model
- Context Length: 262,144 tokens (256K)
- Quantization Options:
- Q4_K_M: 5.3 GB (recommended for most systems)
- Q5_K_M: 6.47 GB
- Q6_K: 7.36 GB
- Q8_0: 9.53 GB
- bf16: 17.9 GB
Ornith-1.0-35B-GGUF (MoE)
- Source: https://huggingface.co/ornith-ai/Ornith-1.0-35B-GGUF
- Architecture: 35B MoE (Mixture of Experts)
- Context Length: 262,144 tokens (256K)
- Quantization Options:
- Q4_K_M: 20 GB (recommended)
- Other quantizations available
Note: No fp4 quantization exists for these models. Q4_K_M is the lightest quantization.
Comparison: 9B vs 35B MoE
| Aspect | 9B Dense | 35B MoE |
|---|---|---|
| Memory (Q4) | 5.3 GB | 20 GB |
| VRAM for Full GPU | 8 GB | 24-40 GB |
| Speed (GPU) | Faster | Slower but better quality |
| Quality | Good | Excellent |
| Context | 256K | 256K |
| Best For | Quick tasks, limited RAM | Complex reasoning, best quality |
Requirements
Hardware
- RAM: Minimum 16GB for 9B model, 32GB+ for 35B model
- GPU (optional but recommended):
- 9B Q4: 6-8GB VRAM for full GPU offload
- 35B Q4: 24GB+ VRAM for full GPU offload
Software
- llama.cpp built with CUDA support (for GPU acceleration)
- Git (for cloning llama.cpp repo)
Related
- Installation — How to set up the server
- GPU Offloading — Layer allocation by VRAM