Aya Expanse 32B needs ~17.7 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q2_K quantization, expect ~9 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
8.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.2 tok/s
TTFT
59759 ms
Safe context
4K
Memory
24.8 GB / 16.0 GB
Offload
40%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.6 tok/s | 29171 ms | 4K |
| Coding | F | Too heavy | 3.2 tok/s | 59759 ms | 4K |
| Agentic Coding | F | Too heavy | 2.6 tok/s | 106859 ms | 4K |
| Reasoning | F | Too heavy | 3.2 tok/s | 70624 ms | 4K |
| RAG | F | Too heavy | 2.6 tok/s | 133574 ms | 4K |
How Aya Expanse 32B (32B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | F0 |
Q3_K_S | 3 | 15.7 GB | Low | F0 |
NVFP4 | 4 |
Copy-paste commands to run Aya Expanse 32B on your machine.
Run
ollama run aya-expanse:32bUpgrade options
RTX 3090 24GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 650%.
~$1,499 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 559%.
~$1,599 MSRP
RTX 5090 32GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,999 MSRP
Yes, NVIDIA T4 16GB can run Aya Expanse 32B at Q2_K quantization (Very compromised (needs ~1.2 GB host RAM)). The recommended Q4_K_M requires 24.8 GB which exceeds available memory, but at Q2_K it needs only 17.7 GB. Expected decode speed: 9.0 tok/s.
Aya Expanse 32B (32B parameters) requires approximately 24.8 GB at Q4_K_M quantization. On NVIDIA T4 16GB, it fits at Q2_K using 17.7 GB.
The recommended quantization is Q4_K_M, but on NVIDIA T4 16GB the best fitting quantization is Q2_K, which uses 17.7 GB.
On NVIDIA T4 16GB, Aya Expanse 32B achieves approximately 9.0 tokens per second decode speed with a time-to-first-token of 21562ms using Q2_K quantization.
For coding workloads, Aya Expanse 32B on NVIDIA T4 16GB receives a F grade with 3.2 tok/s and 4K context.
On NVIDIA T4 16GB, Aya Expanse 32B can safely use up to 5K tokens of context at Q2_K quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/aya-expanse-32b-on-t4-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 19.5 GB | Medium | F0 |
Q5_K_M | 5 | 23.0 GB | High | F0 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.