Tiny Aya Base — Hardware Requirements & GPU Compatibility
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Tiny Aya Base is a 3.3B-parameter open language model from Cohere in the Aya family. At BF16 it needs about 7.37 GB of VRAM — see which GPUs and Macs can run it below.
6.6K downloads
62 likesSpecifications
- Publisher
- Cohere
- Family
- Aya
- Parameters
- 3.3B
- Release Date
- 2026-02-13
- License
- CC BY-NC 4.0
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HuggingFace
How Much VRAM Does Tiny Aya Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 7.4 GB | — | 6.70 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 Tiny Aya Base?
BF16 · 7.4 GBTiny Aya Base (BF16) requires 7.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 10+ GB is recommended. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Tiny Aya Base?
BF16 · 7.4 GB55 devices with unified memory can run Tiny Aya Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Tiny Aya Base need?
Tiny Aya Base requires 7.4 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 3.3B × 16 bits ÷ 8 = 6.7 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF167.4 GB- Can I run Tiny Aya Base on a Mac?
Tiny Aya Base requires at least 7.4 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 Tiny Aya Base locally?
Yes — Tiny Aya Base can run locally on consumer hardware. At BF16 quantization it needs 7.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Tiny Aya Base?
At BF16, Tiny Aya Base can reach ~597 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~89 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 ÷ 7.4 × 0.65 = ~706 tok/s
Estimated speed at BF16 (7.4 GB)
~706 tok/s~89 tok/s~706 tok/s~597 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Tiny Aya Base?
At BF16, the download is about 6.70 GB.
- Which GPUs can run Tiny Aya Base?
50 consumer GPUs can run Tiny Aya Base at BF16 (7.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Tiny Aya Base?
59 devices with unified memory can run Tiny Aya Base at BF16 (7.4 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.