Aya Expanse 32B
By Cohere For AI · United States
Updated 2026-07-13
Overview
The 32B sibling of Aya Expanse from Cohere For AI, delivering a 25% gain on low-resource languages and 89.9% win rate on Dolly vs Mixtral 8x22B. CC-BY-NC.
When to pick this model
- You're doing high-quality multilingual research at the 30B tier
- You need top-tier low-resource language performance
- You're comparing against Mixtral 8x22B on multilingual benchmarks
- Non-commercial use is acceptable for your project
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 19 GB |
| Q5_K_M | 23 GB |
| Q8_0 | 35 GB |
| FP16 (no quantization) | 64 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Aya Expanse 32B wants a 24 GB card at Q4_K_M (19 GB). Stepping up to Q8_0 nearly doubles the footprint to 35 GB, and unquantized FP16 weights take 64 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Aya Expanse 32B needs roughly 32 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 3 tokens/sec on entry-level GPUs, on the order of 12 tokens/sec on a mid-range card, and up to 30 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Aya Expanse 32B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Aya Expanse 32B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 19 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 19 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 19 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (23 GB used) |
| 32 GB | RTX 5090 | Q5_K_M (23 GB used) |
Which GPU should you buy to run Aya Expanse 32B?
To run Aya Expanse 32B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| Dolly (vs Mixtral 8x22B) | 89.9 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- 25% improvement on low-resource languages vs peers
- 23 language coverage
- 89.9% win rate on Dolly vs Mixtral 8x22B
- Strong general performance for its size
Limitations
- CC-BY-NC 4.0 — no commercial use
- Only 8K context window
- Newer Qwen 3 models close much of the gap with permissive licenses
Typical workloads
In our catalog grid, Aya Expanse 32B is filed under Advanced Multilingual, Pro Chat — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads.
Note the 8k-token context window — fine for short interactions, limiting for long documents or big retrieval contexts. It ships under the CC-BY-NC 4.0 license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Dense (Command R base) · 23 languages
Training: Multilingual fine-tune of the Command backbone.
The strongest open multilingual 32B for research — license disqualifies it for production.
Quick start
ollama run aya-expanse:32bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Aya Expanse 32B need?
At the recommended Q4_K_M quantization, Aya Expanse 32B needs about 19 GB of VRAM. Q8_0 takes 35 GB, and unquantized FP16 weights take 64 GB.
Can Aya Expanse 32B run without a GPU?
Yes — with roughly 32 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Aya Expanse 32B support?
Aya Expanse 32B supports a 8k-token context window (8,192 tokens).
Can I use Aya Expanse 32B commercially?
Aya Expanse 32B ships under the CC-BY-NC 4.0 license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Aya Expanse 32B on consumer hardware?
Our compatibility engine estimates on the order of 12 tokens/sec on a mid-range GPU and up to 30 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Aya Expanse 32B should I download first?
Start with Q4_K_M (19 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.