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 ~24 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	23.6 tok/s	8201 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

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

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 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%.

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

~$10,000 MSRP

Frequently asked questions

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

Yes, RTX 5000 Ada 32GB can run Aya Expanse 32B with a B grade (Runs well). Expected decode speed: 23.6 tok/s.

How much VRAM does Aya Expanse 32B need?

Aya Expanse 32B (32B parameters) requires approximately 26.1 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 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Aya Expanse 32B achieves approximately 23.6 tokens per second decode speed with a time-to-first-token of 8201ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Aya Expanse 32B for coding?

For coding workloads, Aya Expanse 32B on RTX 5000 Ada 32GB receives a B grade with 23.6 tok/s and 8K context.

What context window can Aya Expanse 32B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, 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.

See all results for RTX 5000 Ada 32GB See all hardware for Aya Expanse 32B

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