RTX 5090 32GBBudget pick32 GB VRAM (+8)1792 GB/s (+896)
B
Raises estimated decode speed by about 54%.43.3 tok/s decode
Raises estimated decode speed by about 54%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Aya Expanse 32B needs ~25.3 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q4_K_M quantization, expect ~28 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
1.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~1 GB host RAM)
Decode
28.2 tok/s
TTFT
6857 ms
Safe context
8K
Memory
25.3 GB / 24.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~0 GB host RAM) | 31.3 tok/s | 3370 ms | 8K |
| Coding | B | Runs with offload (needs ~1 GB host RAM) | 28.2 tok/s | 6857 ms | 8K |
| Agentic Coding | C | Very compromised (needs ~2.6 GB host RAM) | 23.3 tok/s | 12112 ms | 8K |
| Reasoning | B | Runs with offload (needs ~1 GB host RAM) | 28.2 tok/s | 8104 ms | 8K |
| RAG | C | Very compromised (needs ~2.6 GB host RAM) | 23.3 tok/s |
How Aya Expanse 32B (32B params) fits at each quantization level on RTX 5090 Laptop 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | B56 |
Q3_K_S | 3 | 15.7 GB | Low | B55 |
NVFP4Best for your GPU |
Copy-paste commands to run Aya Expanse 32B on your machine.
Run
ollama run aya-expanse:32bUpgrade options
RTX 5090 32GBBudget pickRaises estimated decode speed by about 54%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueRaises estimated decode speed by about 49%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Yes, RTX 5090 Laptop 24GB can run Aya Expanse 32B with a B grade (Runs with offload (needs ~1 GB host RAM)). Expected decode speed: 28.2 tok/s.
Aya Expanse 32B (32B parameters) requires approximately 25.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Aya Expanse 32B is Q4_K_M, which balances quality and memory efficiency.
On RTX 5090 Laptop 24GB, Aya Expanse 32B achieves approximately 28.2 tokens per second decode speed with a time-to-first-token of 6857ms using Q4_K_M quantization.
For coding workloads, Aya Expanse 32B on RTX 5090 Laptop 24GB receives a B grade with 28.2 tok/s and 8K context.
On RTX 5090 Laptop 24GB, Aya Expanse 32B can safely use up to 8K tokens of context. 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-rtx-5090-laptop-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 15140 ms |
| 8K |
| 4 |
17.9 GB |
| Medium |
| C55 |
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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.