Ouro 1.4B — Hardware Requirements & GPU Compatibility
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Ouro 1.4B is a 1.4B-parameter open language model from ByteDance. It supports a context window of up to 65,536 tokens. At BF16 it needs about 3.57 GB of VRAM — see which GPUs and Macs can run it below.
61.1K downloads
108 likesSpecifications
- Publisher
- ByteDance
- Parameters
- 1.4B
- Architecture
- OuroForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2025-10-28
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Ouro 1.4B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 3.6 GB | 16.1 GB | 2.87 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Ouro 1.4B?
BF16 · 3.6 GBOuro 1.4B (BF16) requires 3.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 66K context window can add up to 12.5 GB, bringing total usage to 16.1 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Ouro 1.4B?
BF16 · 3.6 GB59 devices with unified memory can run Ouro 1.4B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Ouro 1.4B need?
Ouro 1.4B requires 3.6 GB of VRAM at BF16. Full 66K context adds up to 12.5 GB (16.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.4B × 16 bits ÷ 8 = 2.9 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 13.2 GB (at full 66K context)
VRAM usage by quantization
BF163.6 GBBF16 + full context16.1 GB- Can I run Ouro 1.4B on a Mac?
Ouro 1.4B requires at least 3.6 GB at BF16, 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 Ouro 1.4B locally?
Yes — Ouro 1.4B can run locally on consumer hardware. At BF16 quantization it needs 3.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Ouro 1.4B?
At BF16, Ouro 1.4B can reach ~1233 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~184 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 3.6 × 0.65 = ~1457 tok/s
Estimated speed at BF16 (3.6 GB)
~1457 tok/s~184 tok/s~1457 tok/s~1233 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Ouro 1.4B?
At BF16, the download is about 2.87 GB.
- Which GPUs can run Ouro 1.4B?
50 consumer GPUs can run Ouro 1.4B at BF16 (3.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Ouro 1.4B?
59 devices with unified memory can run Ouro 1.4B at BF16 (3.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.