Run deepreinforce-ai/Ornith-1.0-35B-GGUF locally
deepreinforce-ai/Ornith-1.0-35B-GGUF is a very large language model with 34.66 billion parameters, built on the qwen35moe architecture. It is released under the mit license and has been downloaded 359,659 times.
To run deepreinforce-ai/Ornith-1.0-35B-GGUF locally at a 4,096-token context, its quantized versions need between 20.67 GB (Q4_K_M, lowest quality) and 65.57 GB (BF16, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q5_K_M, needing about 23.99 GB. That means deepreinforce-ai/Ornith-1.0-35B-GGUF fits entirely in the VRAM of a 24 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q4_K_M | 4.89 | Good | 19.71 GB | 0.16 GB | 20.67 GB | 2.5 t/s | Offload |
| Q5_K_M | 5.71 | Very good | 23.03 GB | 0.16 GB | 23.99 GB | 2.2 t/s | Offload |
| Q6_K | 6.58 | Excellent | 26.56 GB | 0.16 GB | 27.51 GB | — | Insufficient |
| Q8_0 | 8.52 | Excellent | 34.37 GB | 0.16 GB | 35.32 GB | — | Insufficient |
| BF16 | 16.01 | Excellent | 64.61 GB | 0.16 GB | 65.57 GB | — | Insufficient |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run deepreinforce-ai/Ornith-1.0-35B-GGUF?
You need about 23.99 GB of VRAM to run deepreinforce-ai/Ornith-1.0-35B-GGUF entirely on the GPU using the Q5_K_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run deepreinforce-ai/Ornith-1.0-35B-GGUF on an 8 GB GPU?
Partially. deepreinforce-ai/Ornith-1.0-35B-GGUF only fits on an 8 GB GPU by offloading part of it to system RAM (with Q5_K_M), which runs but is slower.
Can I run deepreinforce-ai/Ornith-1.0-35B-GGUF on a 16 GB GPU?
Partially. deepreinforce-ai/Ornith-1.0-35B-GGUF only fits on a 16 GB GPU by offloading part of it to system RAM (with Q8_0), which runs but is slower.
Can I run deepreinforce-ai/Ornith-1.0-35B-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run deepreinforce-ai/Ornith-1.0-35B-GGUF fully on the GPU using Q5_K_M (about 23.99 GB).
What is the best quantization for deepreinforce-ai/Ornith-1.0-35B-GGUF?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.