Can Aya Expanse 32B run on RTX 5000 Ada 32GB?

YES — Runs Great

B58Good

○Estimated from fit model

Aya Expanse 32B needs ~26.1 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

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) — 26.1 GB, 25.7 tok/s, Runs well

26.1 GB required32.0 GB available

82% VRAM used

Fit status

Runs well

Decode

25.7 tok/s

TTFT

7541 ms

Safe context

Memory

26.1 GB / 32.0 GB

Memory breakdown

Weights19.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsAya Expanse 32B on RTX 5000 Ada 32GB
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: 25.7 tok/s decode · 7.5s TTFT (warm) · 64 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	25.7 tok/s	4113 ms	8K
Coding	B	Runs well	25.7 tok/s	7541 ms	8K
Agentic Coding	B	Tight fit	25.7 tok/s	10969 ms	8K
Reasoning	B	Runs well	25.7 tok/s	8912 ms	8K
RAG	B	Tight fit	25.7 tok/s	13711 ms	8K

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C53
Q3_K_S	3	15.7 GB	Low	C55
NVFP4	4	17.9 GB	Medium	C55
Q4_K_M	4	19.5 GB	Medium	C54
Q5_K_MBest for your GPU	5	23.0 GB	High	C54
Q6_K	6	26.2 GB	High	F0
Q8_0	8	34.2 GB	Very High	F0
F16	16	65.6 GB	Maximum	F0

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 PRO 5000 Blackwell 48GBBudget pick

48 GB VRAM (+16)1344 GB/s (+768)

Raises estimated decode speed by about 145%.62.9 tok/s decode

Raises estimated decode speed by about 145%.

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

~$4,999 MSRP

👁 NVIDIA
NVIDIA A100 40GBBest value

40 GB VRAM (+8)1555 GB/s (+979)

Raises estimated decode speed by about 183%.72.8 tok/s decode

Raises estimated decode speed by about 183%.