Can RTX 5090 run Ornith 1.5 122B?
Partly: part of the model runs from system RAM (CPU offload, per the source). Measured 46.4 tok/s generation with part of the model offloaded to system RAM (TabbyAPI + ExLlamaV3 1.6.2, EXL3 4 bpw, 180/256 experts per layer in RAM, no drafting; source).
Every measured result for Ornith 1.5 122B on RTX 5090
| Build | Quant / setup | Engine | tok/s | |
|---|---|---|---|---|
| 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 CPU offload | source |
Memory: RTX 5090 has 32 GB. Ornith 1.5 122B weights are about 76.5 GB at Q4_K_M (125.1B params × 4.89 bits ÷ 8, before context/KV cache). Arithmetic, not a measurement; long contexts need more. Check other quants in the VRAM calculator.
Speeds are decode (generation) tokens/sec as reported by each source; quant, engine and context differ, so small gaps are noise.
All measured speeds for Ornith 1.5 122B: best home rig for Ornith 1.5 122B.
Other models measured on RTX 5090: DeepSeek V4.1 Flash · GLM-5.3 Flash · Gemma 4 12B · Gemma 4 26B-A4B · Gemma 4 31B · Qwen3.5 9B · Qwen3.6 27B · Qwen3.6 35B-A3B · Qwen3.8 27B · Qwen3.8 Flash-Next · gpt-oss 20B
Not enough VRAM? Rent before you buy. Run Ornith 1.5 122B on a bigger GPU on a rented 24–96 GB cloud GPU by the hour and see the real speed before spending €1,000+. Try it on Runpod
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Referral link: if you sign up with Google and add $10, Runpod gives you and RigBase bonus credit (terms). It never affects our data.