VOOZH about

URL: https://willitrunai.com/can-run/aya-expanse-32b-on-rtx-a4500-20gb

⇱ Aya Expanse 32B on RTX A4500 20GB? No — Alternatives

Can Aya Expanse 32B run on RTX A4500 20GB?

YES — With NVFP4

D38Poor
○Estimated from fit model

Aya Expanse 32B needs ~23.6 GB VRAM. RTX A4500 20GB has 20.0 GB. With NVFP4 quantization, expect ~17 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload
Share:

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.

Aya Expanse 32B at Q4_K_M needs 25.2 GB — too much for RTX A4500 20GB (20.0 GB). Runs at NVFP4 (23.6 GB) with medium quality. 3 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 25.2 GB, exceeds 20.0 GB available
25.2 GB required20.0 GB available
126% VRAM needed

5.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.9 tok/s

TTFT

15049 ms

Safe context

4K

Memory

25.2 GB / 20.0 GB

Offload

20%

Memory breakdown

Weights19.5 GB
KV Cache2.4 GB
Runtime1.2 GB
Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsAya Expanse 32B on RTX A4500 20GB
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: 12.9 tok/s decode · 15.0s TTFT (warm) · 32 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% 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.

Best improvement path

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 2.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatDVery compromised (needs ~3.2 GB host RAM)14.3 tok/s7393 ms4K
CodingFToo heavy12.9 tok/s15049 ms4K
Agentic CodingFToo heavy10.6 tok/s26600 ms4K
ReasoningFToo heavy12.9 tok/s17785 ms4K
RAGFToo heavy10.6 tok/s33250 ms4K

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
12.5 GB
LowB56
Q3_K_S
3
15.7 GB
LowF0
NVFP4
4
17.9 GB
MediumF0
Q4_K_M
4
19.5 GB
MediumF0
Q5_K_M
5
23.0 GB
HighF0
Q6_K
6
26.2 GB
HighF0
Q8_0
8
34.2 GB
Very HighF0
F16
16
65.6 GB
MaximumF0

Get started

Copy-paste commands to run Aya Expanse 32B on your machine.

Run

ollama run aya-expanse:32b

Upgrade options

Hardware that runs Aya Expanse 32B well

👁 NVIDIA
RTX 3090 24GBBudget pick
24 GB VRAM (+4)936 GB/s (+296)
C
Makes the model fit on the accelerator instead of staying completely out of reach.24 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 86%.

~$1,499 MSRP

👁 NVIDIA
RTX 5090 32GBBest value
32 GB VRAM (+12)1792 GB/s (+1152)
B
Makes the model fit on the accelerator instead of staying completely out of reach.66.9 tok/s decode

Makes 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

👁 NVIDIA
RTX A5500 24GBNVIDIA upgrade
24 GB VRAM (+4)768 GB/s (+128)
C
Makes the model fit on the accelerator instead of staying completely out of reach.21.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 70%.

~$3,200 MSRP

Frequently asked questions

See all results for RTX A4500 20GBSee all hardware for Aya Expanse 32B