32 GB VRAM (+8)1792 GB/s (+856)
B
Raises estimated decode speed by about 185%.43.3 tok/s decode
Raises estimated decode speed by about 185%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Aya Expanse 32B run on RTX 3090 24GB?
YES — With Offload
C55Usable
○Estimated from fit model
Aya Expanse 32B needs ~25.3 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~23 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload
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Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 25.3 GB, 15.2 tok/s, Runs with offload (needs ~1 GB host RAM)
25.3 GB required24.0 GB available
105% VRAM needed
1.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~1 GB host RAM)
Decode
15.2 tok/s
TTFT
12746 ms
Safe context
8K
Memory
25.3 GB / 24.0 GB
Memory breakdown
Weights19.5 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsAya Expanse 32B on RTX 3090 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 15.2 tok/s decode · 12.7s TTFT (warm) · 38 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0 GB host RAM) | 16.9 tok/s | 6264 ms | 8K |
| Coding | C | Runs with offload | 22.6 tok/s | 8566 ms | 8K |
| Agentic Coding | C | Very compromised (needs ~2.6 GB host RAM) | 12.5 tok/s | 22513 ms | 8K |
| Reasoning | C | Runs with offload (needs ~1 GB host RAM) | 15.2 tok/s | 15063 ms | 8K |
| RAG | C | Very compromised (needs ~2.6 GB host RAM) | 12.5 tok/s |
Quantization options
How Aya Expanse 32B (32B params) fits at each quantization level on RTX 3090 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 |
Get started
Copy-paste commands to run Aya Expanse 32B on your machine.
Run
ollama run aya-expanse:32bUpgrade options
Hardware that runs Aya Expanse 32B well
32 GB VRAM (+8)
B
Raises estimated decode speed by about 176%.41.9 tok/s decode
Raises estimated decode speed by about 176%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
32 GB VRAM (+8)
B
Raises estimated decode speed by about 69%.25.7 tok/s decode
Raises estimated decode speed by about 69%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP