Models / Ornith 1.5 122B
Best home rig for Ornith 1.5 122B
Current model125.1B total · 10B active1 measured resultfastest plain decode: 46.4 tok/s model info ↗
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Real, measured generation speeds for Ornith 1.5 122B on home hardware, fastest first. Different quants/engines aren't directly comparable.
| Build | Quant | Engine | tok/s | Cost | Source |
|---|---|---|---|---|---|
| Ryzen 9 9950X3D + RTX 5090 + 128 GB DDR5: four big MoEs with experts offloaded to RAM (Qwen3.8 Flash-Next 78.8 tok/s) | EXL3 4 bpw, 180/256 experts per layer in RAM, no drafting | TabbyAPI + ExLlamaV3 1.6.2 | 46.4 | dev.to ↗ |
Memory math: how much VRAM / RAM does Ornith 1.5 122B need?
Ornith 1.5 122B has 125.1B parameters, about 10B active per token (source: Hugging Face: 125B incl. vision tower + MTP layer (122B language model, 10B active per halt95/Ornith-1.5-122B-A10B card)). Weight size = parameters × bits-per-weight ÷ 8, before context (KV cache) and runtime overhead.
The source benchmark's EXL3 4 bpw quant is a 65.5 GB download (dev.to, Seppe Gadeyne).
| Format | Bits/weight | Weights | Smallest common memory size that holds the weights alone |
|---|---|---|---|
| Q8_0 | 8.50 | 132.9 GB | 192 GB |
| Q6_K | 6.56 | 102.6 GB | 128 GB |
| Q5_K_M | 5.70 | 89.2 GB | 96 GB |
| Q4_K_M | 4.89 | 76.5 GB | 96 GB |
| Q3_K_M | 4.00 | 62.5 GB | 64 GB |
| Q2_K | 3.16 | 49.4 GB | 64 GB |
| As measured: EXL3 4 bpw, 180/256 experts per layer in RAM, no drafting | 4.00 | ≈62.5 GB | 64 GB |
Bits-per-weight for GGUF quants are llama.cpp's own figures (llama.cpp quantize README). "As measured" rows apply the source's stated bits-per-weight to the parameter count; real files differ slightly because some layers stay at higher precision. Try other combinations in the VRAM calculator.
Why it can still run on smaller GPUs: it is a mixture-of-experts model, so only about 10B of its 125.1B parameters are used for each generated token. Runtimes such as llama.cpp (--n-cpu-moe), ExLlamaV3 or Project Maya can keep most expert weights in system RAM and only the shared layers and hot experts in VRAM. Then you need VRAM + system RAM ≥ weights, and speed is limited mainly by RAM bandwidth.
Measured on setups with less GPU memory than the Q4_K_M weights (so part of the model lived in system RAM or was streamed from disk):
- RTX 5090 (32 GB) + 128 GB DDR5-5600 (4x32 GB): 46.4 tok/s (EXL3 4 bpw, 180/256 experts per layer in RAM, no drafting)
FAQ
How much VRAM does Ornith 1.5 122B need?
The weights are about 76.5 GB at Q4_K_M (125.1B params × 4.89 bits ÷ 8, before context/KV cache). Add memory for context (KV cache) and the runtime.
What is the fastest measured hardware for Ornith 1.5 122B?
RTX 5090: 46.4 tok/s generation (TabbyAPI + ExLlamaV3 1.6.2, EXL3 4 bpw, 180/256 experts per layer in RAM, no drafting), from https://dev.to/seppegadeyne/four-big-local-llms-on-one-rtx-5090-and-128-gb-of-ram-benchmarked-side-by-side-2h8j.
Which GPUs have measured results for Ornith 1.5 122B?
RTX 5090.
Can it run on… RTX 5090
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