ornith-aiOrnithQwen3_5MoeForConditionalGeneration

Ornith 1.5 122B — Hardware Requirements & GPU Compatibility

ChatFunctions

Ornith-1.5-122B is a mixture-of-experts language model from ornith-ai, the largest-capability member of the Ornith-1.5 family. It extends Ornith-1.0, which was built on Qwen3.5 and Gemma 4, and the team describes it as the product of an end-to-end self-improvement loop covering task generation, scaffolds, and solution rollouts. It has about 125 billion total parameters with roughly 12.7 billion active per token. A Q4 build needs roughly 71 GB of memory, and the card says the model serves on a single 80 GB GPU. The context window is 262,144 tokens, and the card describes extending it to roughly 1 million with YaRN scaling. It is released under the MIT license, which permits commercial use, and was published on October 16, 2026. The card reports results on Terminal-Bench 2.1, SWE-Bench Verified and Pro, MCP-Atlas, and Toolathlon-Verified. It gives vLLM and SGLang serving recipes with tool-call and reasoning parsers, and a quantized Mobile variant exists for edge devices.

125.1B params · MoE13B active262K contextMITOct 202614.0K downloads149 likes25.1K quant downloads

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

Detection reads your browser's GPU name with no data sent anywhere.

Ornith 1.5 122B runs on a 96 GB+ Mac or a workstation GPU

Best local pick → Mac Studio (M3 Ultra, 96GB) · Q4_K_M · 75.5 GB · ~44 tok/s

Macs from 48 GB (IQ2_M · ~51 tok/s)

MIT

Not set up for it?

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run Ornith 1.5 122B

One recommended way. Every other method, OS and quant is in the step-by-step guide.

Other ways

each opens step-by-step

Run in cloud

Fits on RTX PRO 6000 (96 GB) (20 GB headroom) · Q4_K_M

Generation speed: ~108 tok/s; generation speed
Cost per 1M output tokens: $3.43; per 1M output tokens

Deployon Vast.aiBest optionfrom $1.34/hr

Compare GPUs →

Run Ornith 1.5 122B

75.5 GB VRAM at 16K context · needs offload on the RTX 4090 (51.5 GB short)

Works on macOS, Windows and Linux.

Install Ollama (Linux)
$curl -fsSL https://ollama.com/install.sh | sh
Install Ollama (macOS / Windows)
Download and run the app; it installs the ollama command.
Download Ollama ↗
Run Q4_K_M
$ollama run hf.co/bartowski/Ornith-1.5-122B-GGUF:Q4_K_M
Raise the context window
Type it inside the chat. For the API server instead, start it with OLLAMA_CONTEXT_LENGTH=16384 ollama serve.
$/set parameter num_ctx 16384
A larger context needs more memory than the weights alone.
Use the API
Ollama serves its API on http://localhost:11434.

Works on macOS, Windows and Linux.

Install LM Studio
Desktop app for macOS (Apple silicon), Windows and Linux.
lmstudio.ai ↗
Download the model
In the app press Ctrl/Cmd + Shift + M and paste https://huggingface.co/bartowski/Ornith-1.5-122B-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/bartowski/Ornith-1.5-122B-GGUF@Q4_K_M
Open on Hugging Face ↗
Load and chat
Open the model loader, select the model and set Context Length to 16384. Leave GPU offload at maximum if it fits.
Start the local server (optional)
$lms server start

Works on macOS, Windows and Linux.

Install llama.cpp (macOS)
MacPorts, Nix and conda-forge also work.
$brew install llama.cpp
Install options ↗
Install llama.cpp (Windows)
$winget install llama.cpp
Install llama.cpp (Linux)
Download the prebuilt llama-*-bin-ubuntu-* archive for your GPU backend (CPU, Vulkan) from the releases page, unpack it and run the binaries from that folder. Homebrew (brew install llama.cpp) and conda-forge also work on Linux.
llama.cpp releases ↗
Start the server
Downloads the model on first run. The chat UI and an OpenAI-compatible API are at http://localhost:8080.
$llama-server -hf bartowski/Ornith-1.5-122B-GGUF:Q4_K_M -c 16384 -ngl 99 --port 8080

Works on macOS, Windows and Linux.

Option A: Docker Model Runner
Docker Desktop: Settings > AI > Enable Docker Model Runner. Docker Engine on Linux: install the docker-model-plugin package.
$docker model run hf.co/bartowski/Ornith-1.5-122B-GGUF:Q4_K_M
Model Runner docs ↗
The quant tag after the repo is how Hugging Face cards write it; if your Docker version rejects it, drop the tag and pick the quant by repo.
Option B: llama.cpp server image (NVIDIA)
Needs the NVIDIA Container Toolkit. Models are cached in the llama-cache volume. Open http://localhost:8080.
$docker run --gpus all -p 8080:8080 -v llama-cache:/root/.cache ghcr.io/ggml-org/llama.cpp:server-cuda -hf bartowski/Ornith-1.5-122B-GGUF:Q4_K_M -c 16384 -ngl 99 --host 0.0.0.0 --port 8080
Image tags (cuda13, vulkan, rocm) ↗
Cache path inside the image is not in the docs; remove the -v flag if the volume complains.

Linux only.

Install vLLM
Linux only; on Windows run it inside WSL2. NVIDIA GPU shown; AMD ROCm, Intel and CPU builds have their own install pages.
$uv venv --python 3.12 --seed
$source .venv/bin/activate
$uv pip install vllm --torch-backend=auto
vLLM installation ↗
Serve the model
OpenAI-compatible API at http://localhost:8000/v1.
$vllm serve ornith-ai/Ornith-1.5-122B --max-model-len 16384
Runs the original weights, not the Q4_K_M GGUF; needs ~275 GB of GPU memory.

macOS only.

MLX supports only macOS, so follow these steps for that setup.

Install mlx-lm
$pip install mlx-lm
Chat in the terminal
$mlx_lm.chat --model ezoushen/ornith-1.5-122b-mlx-4bit
Downloads the model on first run. The MLX repo has its own quantisation; it may not match the quant selected above.
Or start a local server
Listens on http://localhost:8080.
$mlx_lm.server --model ezoushen/ornith-1.5-122b-mlx-4bit

Works on macOS, Windows and Linux.

Install Jan
jan.ai ↗
Find the model
Open the Hub tab, paste this model ID in Search, and choose Q4_K_M if offered.
$bartowski/Ornith-1.5-122B-GGUF
Use the local API (optional)
Settings > Local API Server > Start Server. It listens on http://127.0.0.1:1337.

Rent GPUs hourly to run Q4_K_M right there. Pricing comes from partner marketplaces and varies.

Available GPU rentals for Q4_K_M, prioritized by value
GPU	Speed	$/1M tokens	Rent
RTX PRO 6000 (96 GB)CheapestCheapest20 GB free	~108 tok/s	$3.43	Deploy on Vast.aifrom $1.34/hr (opens in new tab)	96 GB · 20 GB free · ~108 tok/s · $3.43 / 1M tokens
RTX PRO 6000 Max-Q (96 GB)20 GB free	~108 tok/s	$3.43	Deploy on Vast.aifrom $1.34/hr (opens in new tab)	96 GB · 20 GB free · ~108 tok/s · $3.43 / 1M tokens
RTX PRO 6000 Server (96 GB)20 GB free	~100 tok/s	$4.10	Deploy on Vast.aifrom $1.47/hr (opens in new tab)	96 GB · 20 GB free · ~100 tok/s · $4.10 / 1M tokens
RTX PRO 6000 (96 GB)20 GB free	~108 tok/s	$4.34	Deploy on RunPodfrom $1.69/hr (opens in new tab)	96 GB · 20 GB free · ~108 tok/s · $4.34 / 1M tokens
RTX PRO 6000 Server (96 GB)20 GB free	~100 tok/s	$4.71	Deploy on RunPodfrom $1.69/hr (opens in new tab)	96 GB · 20 GB free · ~100 tok/s · $4.71 / 1M tokens
H200 SXM (141 GB)65 GB free	~196 tok/s	$5.09	Deploy on RunPodfrom $3.59/hr (opens in new tab)	141 GB · 65 GB free · ~196 tok/s · $5.09 / 1M tokens
H200 NVL (141 GB)65 GB free	~196 tok/s	$5.12	Deploy on Vast.aifrom $3.61/hr (opens in new tab)	141 GB · 65 GB free · ~196 tok/s · $5.12 / 1M tokens
B200 (192 GB)116 GB free	~243 tok/s	$6.84	Deploy on RunPodfrom $5.98/hr (opens in new tab)	192 GB · 116 GB free · ~243 tok/s · $6.84 / 1M tokens

Compare GPUs →

How Much VRAM Does Ornith 1.5 122B Need?

Select a quantization level; the listed hardware adheres to it.

VRAM	+32K	+128K	File	Quality	On RTX 4090 (reference)
34.8 GB	35 GB	35.9 GB	42 GB	2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss	Does not fit: needs 34.9 GB
37.9 GB	38.1 GB	39 GB	42.8 GB	2-bit · i-quant extra small — Importance-weighted 2-bit, extra small	Does not fit: needs 38 GB
39.5 GB	39.7 GB	40.6 GB	47.6 GB	2-bit · i-quant small — Importance-weighted 2-bit, small	Does not fit: needs 39.6 GB
42.6 GB	42.8 GB	43.7 GB	46.4 GB	2-bit · i-quant medium — Importance-weighted 2-bit, medium	Does not fit: needs 42.7 GB
48.9 GB	49.1 GB	50 GB	49.5 GB	3-bit · i-quant — Importance-weighted 3-bit	Does not fit: needs 48.9 GB
52.0 GB	52.2 GB	53.1 GB	57.9 GB	3-bit · i-quant extra small — Importance-weighted 3-bit, extra small	Does not fit: needs 52.1 GB
53.5 GB	53.8 GB	54.7 GB	62.2 GB	3-bit · i-quant small — Importance-weighted 3-bit, small	Does not fit: needs 53.6 GB
53.5 GB	53.8 GB	54.7 GB	48.9 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Does not fit: needs 53.6 GB
55.1 GB	55.3 GB	56.2 GB	57.5 GB	3-bit small — 3-bit small quantization	Does not fit: needs 55.2 GB
56.7 GB	56.9 GB	57.8 GB	67.6 GB	3-bit · i-quant medium — Importance-weighted 3-bit, medium	Does not fit: needs 56.7 GB
61.4 GB	61.6 GB	62.5 GB	60.6 GB	3-bit medium — 3-bit medium quantization	Does not fit: needs 61.4 GB
62.9 GB	63.2 GB	64.1 GB	71.3 GB	4-bit · legacy — 4-bit legacy quantization	Does not fit: needs 63 GB
64.5 GB	64.7 GB	65.6 GB	64.2 GB	3-bit large — 3-bit large quantization	Does not fit: needs 64.6 GB
67.6 GB	67.8 GB	68.7 GB	71.2 GB	4-bit · i-quant compact — Importance-weighted 4-bit, compact	Does not fit: needs 67.7 GB
70.7 GB	71 GB	71.9 GB	78.9 GB	4-bit · i-quant non-linear — Importance-weighted 4-bit, non-linear	Does not fit: needs 70.8 GB
70.7 GB	71 GB	71.9 GB	78.5 GB	4-bit · legacy+ — 4-bit legacy quantization with offset	Does not fit: needs 70.8 GB
70.7 GB	71 GB	71.9 GB	74.2 GB	4-bit small — 4-bit small quantization	Does not fit: needs 70.8 GB
75.4 GB	75.7 GB	76.6 GB	78.9 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Does not fit: needs 75.5 GB
77.0 GB	77.2 GB	78.1 GB	84.7 GB	4-bit large — 4-bit large quantization	Does not fit: needs 77.1 GB
86.4 GB	86.6 GB	87.5 GB	89.1 GB	5-bit small — 5-bit small quantization	Does not fit: needs 86.5 GB
89.5 GB	89.7 GB	90.6 GB	94.9 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Does not fit: needs 89.6 GB
103.6 GB	103.8 GB	104.7 GB	104.5 GB	6-bit, very good — 6-bit quantization, very good quality	Does not fit: needs 103.7 GB
125.5 GB	125.7 GB	126.6 GB	132.6 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Does not fit: needs 125.5 GB
250.6 GB	250.8 GB	251.7 GB	249.4 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Does not fit: needs 250.6 GB

Selected Q4_K_M

4-bit medium quantization

75.5GBat 16K context

Weights: 75.1 GB
KV cache: 0.2 GB
Overhead: 0.3 GB

Doesn't fit RTX 4090 (reference) · needs 51.5 GB more

≥30 tok/s needs ≥385 GB/s · M3 Ultra ~44, M2 Ultra ~43

Or rent RTX PRO 6000 from $1.34/hr.

Which Hardware Can Run Ornith 1.5 122B?

Q4_K_M · 75.4 GB

Ornith 1.5 122B (Q4_K_M) requires 75.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 99+ GB is recommended. Using the full 262K context window can add up to 2.3 GB, bringing total usage to 77.8 GB. No consumer GPU has enough memory. 30 devices with unified memory can run Ornith 1.5 122B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio (M3 Ultra, 96GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M

Typical hardwareQuantVRAMtok/sContext that fits

8–16 GB · RTX 4060, RTX 3060 12GB, RTX 5060 Ti 16GBDoesn't fit — smallest quant needs 34.9 GB8 GB tier →12 GB tier →16 GB tier →

24 GB · RTX 4090referenceDoesn't fit — smallest quant needs 34.9 GB24 GB tier →

Mac 32 GB · Mac Mini M4Doesn't fit — smallest quant needs 34.9 GBMacs →

Mac 64 GB · Mac Studio M4 MaxIQ3_M · 56.7 GB · ~41 tok/s · 256K56.7~41256KMacs →

Alternatives to Ornith 1.5 122B

Ornith family · 12 models →

Similar models

6tok/s on RTX 4090

Model	Score	VRAM	tok/s	Why it is similar
Qwen3.5 122B A10BAlibaba	109.4	75.5 GB	—needs > 24 GB	same size
GPT OSS 120BOpenAI	97.5	70.8 GB	—needs > 24 GB	same size
NVIDIA Nemotron 3.5 Super VL 120B A12B BF16NVIDIA	—	87.7 GB	—needs > 24 GB	same size
Ling 3.0 Flash VLInclusion AI	—	76 GB	—needs > 24 GB	same size
Leanstral 1.5 119B A6BMistral AI	—	84 GB	—needs > 24 GB	same size
Laguna S 2.1poolside	—	72.5 GB	—needs > 24 GB	same size

Specifications

Publisher: ornith-ai
Family: Ornith
Parameters: 125.1B
Active parameters: 12.7B per token
Architecture: Qwen3_5MoeForConditionalGeneration
Context Length: 262,144 tokens
Vocabulary Size: 248,320
Release Date: 2026-10-16
License: MIT

Resources

Frequently Asked Questions

How much VRAM does Ornith 1.5 122B need?

Ornith 1.5 122B requires 75.4 GB of VRAM at Q4_K_M, or 250.6 GB at BF16. Full 262K context adds up to 2.3 GB (77.8 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 125.1B × 4.8 bits ÷ 8 = 75.1 GB

KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)

Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.

KV Cache + Overhead ≈ 2.7 GB (at full 262K context)

VRAM usage by quantization

Q4_K_M

75.4 GB

Q4_K_M + full context

77.8 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run Ornith 1.5 122B?

No — Ornith 1.5 122B requires at least 34.8 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for Ornith 1.5 122B?

For Ornith 1.5 122B, Q4_K_M (75.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (77.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 34.8 GB.

VRAM requirement by quantization

IQ2_XXS

34.8 GB~53%

IQ3_S

53.5 GB~75%

Q3_K_L

64.5 GB~86%

Q4_K_M ★

75.4 GB~89%

Q4_K_L

77.0 GB~90%

BF16

250.6 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Ornith 1.5 122B on a Mac?

Yes — Mac Studio M5 Max (48 GB) and 55 other Macs can run Ornith 1.5 122B, the smallest at IQ2_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 75.4 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 96GB).

Can I run Ornith 1.5 122B locally?

Yes — Ornith 1.5 122B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Ornith 1.5 122B?

At Q4_K_M, Ornith 1.5 122B can reach ~89 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)

Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.

Example: NVIDIA B200 → 7.7 GB active ÷ (8000 × 0.65) = 1.48 ms, plus 48 layers × 0.055 ms = 2.64 ms, so 1000 ÷ 4.12 ms = ~243 tok/s

Estimated speed at Q4_K_M (75.4 GB)

NVIDIA B200

~243 tok/s

NVIDIA B300

~243 tok/s

NVIDIA GH200 Grace Hopper Superchip

~198 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Ornith 1.5 122B?

At Q4_K_M, the download is about 75.05 GB. The full-precision BF16 version is 250.17 GB. The smallest option (IQ2_XXS) is 34.40 GB.

Which GPUs can run Ornith 1.5 122B?

No single consumer GPU has enough VRAM to run Ornith 1.5 122B at Q4_K_M (75.4 GB). Multi-GPU or professional hardware is required.

Which devices can run Ornith 1.5 122B?

30 devices with unified memory can run Ornith 1.5 122B at Q4_K_M (75.4 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.