text model · Ornith · Windows
Can I run Ornith 1.0 35B on Nvidia GeForce RTX 5090 (32GB)?
Yes. Ornith 1.0 35B runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~23.2 GB of ~31 GB usable).
Runs at Q4_K_M using ~23.2 GB of ~31 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 5090 (32GB) leaves ~7.8 GB of headroom.
- Q4_K_M needed
- ~23.2 GB
- Usable on device
- ~31 GB
- Device memory
- 32 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run ornith:35b llama-cli -hf deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M lms get deepreinforce-ai/Ornith-1.0-35B-GGUF - Parameters
- 35B (MoE, 3B active)
- Q4_K_M size
- 21 GB
- Q8_0 size
- 37 GB
- Context
- 256k
- Ollama tag
- ornith:35b
- Memory
- 32 GB vram
- Usable for weights
- ~31 GB
- Power draw
- ~575 W
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Ornith 1.0 35B on other hardware
FAQ
Can Nvidia GeForce RTX 5090 (32GB) run Ornith 1.0 35B?
Yes. Ornith 1.0 35B runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~23.2 GB of ~31 GB usable).
How much memory does Ornith 1.0 35B need?
Nvidia GeForce RTX 5090 (32GB) has room to spare. At Q4_K_M the weights are ~21 GB; with KV cache and runtime overhead, budget ~23.2 GB at a 4k context. It is a Mixture-of-Experts model (35B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Ornith 1.0 35B on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/ornith-1.0-35b/nvidia-rtx-5090-32gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.