Deep Reinforce·Ornith·Qwen3_5MoeForConditionalGeneration

Ornith 1.0 397B — Hardware Requirements & GPU Compatibility

Chat

Ornith 1.0 397B is a 396.8B-parameter open language model from Deep Reinforce in the Ornith family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 238.51 GB of VRAM — see which GPUs and Macs can run it below.

260.8K downloads 242 likes 1.2K quant downloads262K context

Specifications

Publisher
Deep Reinforce
Family
Ornith
Parameters
396.8B
Architecture
Qwen3_5MoeForConditionalGeneration
Context Length
262,144 tokens
Vocabulary Size
248,320
Release Date
2026-06-23
License
MIT

Get Started

How Much VRAM Does Ornith 1.0 397B Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.40169.1 GB
Q3_K_M3.90193.9 GB
Q4_K_M4.80238.5 GB
Q5_K_M5.70283.1 GB
Q6_K6.60327.8 GB
Q8_08.00397.2 GB

Which GPUs Can Run Ornith 1.0 397B?

Q4_K_M · 238.5 GB

Ornith 1.0 397B (Q4_K_M) requires 238.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 311+ GB is recommended. Using the full 262K context window can add up to 16.0 GB, bringing total usage to 254.5 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.

Which Devices Can Run Ornith 1.0 397B?

Q4_K_M · 238.5 GB

3 devices with unified memory can run Ornith 1.0 397B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Where to Download Ornith 1.0 397B

Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.

Related Models

Frequently Asked Questions

How much VRAM does Ornith 1.0 397B need?

Ornith 1.0 397B requires 238.5 GB of VRAM at Q4_K_M, or 794.0 GB at BF16. Full 262K context adds up to 16.0 GB (254.5 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 396.8B × 4.8 bits ÷ 8 = 238.1 GB

KV Cache + Overhead 0.4 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 16.4 GB (at full 262K context)

VRAM usage by quantization

238.5 GB
254.5 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run Ornith 1.0 397B?

No — Ornith 1.0 397B requires at least 109.5 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for Ornith 1.0 397B?

For Ornith 1.0 397B, Q4_K_M (238.5 GB) offers the best balance of quality and VRAM usage. Q5_K_S (273.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 109.5 GB.

VRAM requirement by quantization

IQ2_XXS
109.5 GB
IQ3_S
169.1 GB
IQ4_NL
223.6 GB
Q4_K_M
238.5 GB
Q5_K_M
283.1 GB
BF16
794.0 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Ornith 1.0 397B on a Mac?

Ornith 1.0 397B requires at least 109.5 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.

Can I run Ornith 1.0 397B locally?

Yes — Ornith 1.0 397B can run locally on consumer hardware. At Q4_K_M quantization it needs 238.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Ornith 1.0 397B?

At Q4_K_M, Ornith 1.0 397B can reach ~18 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: NVIDIA B3008000 ÷ 238.5 × 0.65 = ~22 tok/s

Estimated speed at Q4_K_M (238.5 GB)

~22 tok/s
~18 tok/s
~18 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 1.0 397B?

At Q4_K_M, the download is about 238.08 GB. The full-precision BF16 version is 793.60 GB. The smallest option (IQ2_XXS) is 109.12 GB.

Which GPUs can run Ornith 1.0 397B?

No single consumer GPU has enough VRAM to run Ornith 1.0 397B at Q4_K_M (238.5 GB). Multi-GPU or professional hardware is required.

Which devices can run Ornith 1.0 397B?

4 devices with unified memory can run Ornith 1.0 397B at Q4_K_M (238.5 GB), including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.