Ouro 2.6B Thinking — Hardware Requirements & GPU Compatibility
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Ouro 2.6B Thinking is a 2.6B-parameter open language model from ByteDance. It supports a context window of up to 65,536 tokens. At BF16 it needs about 6.31 GB of VRAM — see which GPUs and Macs can run it below.
17.2K downloads
96 likesSpecifications
- Publisher
- ByteDance
- Parameters
- 2.6B
- Architecture
- OuroForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2026-02-26
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Ouro 2.6B Thinking Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 6.3 GB | 31.3 GB | 5.20 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Ouro 2.6B Thinking?
BF16 · 6.3 GBOuro 2.6B Thinking (BF16) requires 6.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 9+ GB is recommended. Using the full 66K context window can add up to 25.0 GB, bringing total usage to 31.3 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Ouro 2.6B Thinking?
BF16 · 6.3 GB33 devices with unified memory can run Ouro 2.6B Thinking, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Ouro 2.6B Thinking need?
Ouro 2.6B Thinking requires 6.3 GB of VRAM at BF16. Full 66K context adds up to 25.0 GB (31.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 2.6B × 16 bits ÷ 8 = 5.2 GB
KV Cache + Overhead ≈ 1.1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 26.1 GB (at full 66K context)
VRAM usage by quantization
BF166.3 GBBF16 + full context31.3 GB- Can I run Ouro 2.6B Thinking on a Mac?
Ouro 2.6B Thinking requires at least 6.3 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 2.6B Thinking locally?
Yes — Ouro 2.6B Thinking can run locally on consumer hardware. At BF16 quantization it needs 6.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Ouro 2.6B Thinking?
At BF16, Ouro 2.6B Thinking can reach ~462 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~104 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 6.3 × 0.55 = ~462 tok/s
Estimated speed at BF16 (6.3 GB)
~462 tok/s~104 tok/s~345 tok/s~286 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Ouro 2.6B Thinking?
At BF16, the download is about 5.20 GB.
- Which GPUs can run Ouro 2.6B Thinking?
35 consumer GPUs can run Ouro 2.6B Thinking at BF16 (6.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Ouro 2.6B Thinking?
33 devices with unified memory can run Ouro 2.6B Thinking at BF16 (6.3 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.