Ornith-1.5 122B VRAM Requirements

Ornith-1.5 122B at a glance

Made by

DeepReinforce, United States

Released

October 20, 2026

Parameters

125.1B, 9B active

Context window

256K tokens

License

MIT, commercial use allowed

On Hugging Face

10 likes, 32 downloads last month (opens in a new tab)

Runs with

vLLM, SGLang, llama.cpp (Ollama, LM Studio), MLX (Mac)

VRAM by precision and context length

Weights plus the KV cache of one request (BF16 cache) and the model's fixed recurrent state, in GiB. Add 1 to 3 GiB for the inference engine itself.

Ornith-1.5 122B GGUF files

Exact size of each quantization in ornith-ai/Ornith-1.5-122B-GGUF (opens in a new tab), and the smallest GPU that runs it with llama.cpp, Ollama or LM Studio and an 8K-token context.

Read every day from Hugging Face. Split files are added together; vision projectors are left out.

How many GPUs to run Ornith-1.5 122B

Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.

8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size.

Cheapest way to run Ornith-1.5 122B today

For each GPU: the fewest cards that fit Ornith-1.5 122B with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-20).

On-demand prices from AWS, Google Cloud, Azure, RunPod, Vast.ai, Oracle Cloud, OVHcloud, Scaleway, Verda, Akash and UpCloud, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.

AWS, Google Cloud and UpCloud prices: Spare Cores (opens in a new tab), CC BY-SA 4.0 (opens in a new tab), shared under the same license.

KV cache: how memory grows with context

Architecture: 48 layers · 12 full attention (2 KV × 256) · 36 linear attention. Its KV cache is 4.0× smaller than if every layer used full attention, because only 12 of its 48 layers keep a KV cache; the others hold a small fixed-size state. Only 9B parameters are active per token, but all 125B must sit in memory: VRAM depends on the total, speed on the active part.

Plus 147 MiB of recurrent state per request, whatever the context.

Ornith-1.5 122B VRAM badge

For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/ornith-1-5-122b.json.

[![Ornith-1.5 122B VRAM](https://studiotvai.com/badge/ornith-1-5-122b.svg)](https://studiotvai.com/vram-requirements/ornith-1-5-122b)

Other models

Same family (hybrid linear attention): Qwen3.5 4B · MiniCPM-V-4.7 1B · Qwen3.5 9B · Qwen3.8 27B · Qwen3.6 35B-A3B · Qwen3.5 122B-A10B · Qwen3.8-Flash-Next · LFM2.5 8B-A1B · LFM2 24B-A2B · Ornith 1.5 35B-A3B · Ornith 1.5 9B · Clef · Clef Flash · D1 3B · JEV 27B VL · LightOnOCR-3 4B · Agnes-3.0-Qwen · Agnes-3.0-Flash · Qwen3.6 27B · Qwen3.5 27B · Qwen3.5 35B-A3B · MiniCPM-V-4.7 35B-A3B · LFM2.5 24B-A2B

Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.