text model · Ornith · Windows
Can I run Ornith 1.0 35B on 32GB RAM Laptop (CPU/iGPU only)?
Yes. Ornith 1.0 35B runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~23.2 GB of ~28 GB usable).
Runs at Q4_K_M using ~23.2 GB of ~28 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. 32GB RAM Laptop (CPU/iGPU only) leaves ~4.8 GB of headroom.
- Q4_K_M needed
- ~23.2 GB
- Usable on device
- ~28 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 ram
- Usable for weights
- ~28 GB
- Power draw
- ~28 W
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run Ornith 1.0 35B on other hardware
FAQ
Can 32GB RAM Laptop (CPU/iGPU only) run Ornith 1.0 35B?
Yes. Ornith 1.0 35B runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~23.2 GB of ~28 GB usable).
How much memory does Ornith 1.0 35B need?
32GB RAM Laptop (CPU/iGPU only) 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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en.wikipedia.org · 1 source
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lmstudio.ai · 1 source
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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.