Will It Run AI

Can Aya Expanse 32B run on RTX 4090 24GB?

YES — With Offload

B55Good
Estimated from fit model

Aya Expanse 32B needs ~25.3 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~26 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 25.3 GB, 16.6 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

16.6 tok/s

TTFT

11696 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 4090 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: 16.6 tok/s decode · 11.7s TTFT (warm) · 41 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload (needs ~0 GB host RAM)18.4 tok/s5748 ms8K
CodingBRuns with offload26.4 tok/s7326 ms8K
Agentic CodingCVery compromised (needs ~2.6 GB host RAM)13.6 tok/s20658 ms8K
ReasoningCRuns with offload (needs ~1 GB host RAM)16.6 tok/s13822 ms8K
RAGCVery compromised (needs ~2.6 GB host RAM)13.6 tok/s25823 ms8K

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on RTX 4090 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowB56
Q3_K_S
3
15.7 GB
LowB55
NVFP4Best for your GPU
4
17.9 GB
MediumC55
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

アップグレードオプション

Aya Expanse 32Bを快適に動かすハードウェア

NVIDIARTX 5090 32GBコスパ重視
32 GB VRAM (+8)1792 GB/s (+784)
B
Raises estimated decode speed by about 161%.43.3 tok/sデコード

Raises estimated decode speed by about 161%.

Adds memory headroom for longer context windows and future model growth.

〜$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBコスパ最良
32 GB VRAM (+8)
B
Raises estimated decode speed by about 152%.41.9 tok/sデコード

Raises estimated decode speed by about 152%.

Adds memory headroom for longer context windows and future model growth.

〜$2,499 MSRP

NVIDIARTX 5000 Ada 32GBNVIDIAアップグレード
32 GB VRAM (+8)
B
Raises estimated decode speed by about 55%.25.7 tok/sデコード

Raises estimated decode speed by about 55%.

Adds memory headroom for longer context windows and future model growth.

〜$4,000 MSRP

Frequently asked questions

Can RTX 4090 24GB run Aya Expanse 32B?

Yes, RTX 4090 24GB can run Aya Expanse 32B with a B grade (Runs with offload). Expected decode speed: 26.4 tok/s.

How much VRAM does Aya Expanse 32B need?

Aya Expanse 32B (32B parameters) requires approximately 25.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Aya Expanse 32B?

The recommended quantization for Aya Expanse 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Aya Expanse 32B run at on RTX 4090 24GB?

On RTX 4090 24GB, Aya Expanse 32B achieves approximately 26.4 tokens per second decode speed with a time-to-first-token of 7326ms using Q4_K_M quantization.

Can RTX 4090 24GB run Aya Expanse 32B for coding?

For coding workloads, Aya Expanse 32B on RTX 4090 24GB receives a B grade with 26.4 tok/s and 8K context.

What context window can Aya Expanse 32B use on RTX 4090 24GB?

On RTX 4090 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.

What should I upgrade first if Aya Expanse 32B feels slow on RTX 4090 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 4090 24GBSee all hardware for Aya Expanse 32B
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