Can Aya Expanse 32B run on RTX 5070 Ti 16GB?

YES — With Q2_K

C45Usable

Estimated from fit model

Aya Expanse 32B needs ~17.7 GB VRAM. RTX 5070 Ti 16GB has 16.0 GB. With Q2_K quantization, expect ~26 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Host offload

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 24.8 GB — too much for RTX 5070 Ti 16GB (16.0 GB). Runs at Q2_K (17.7 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 24.8 GB, exceeds 16.0 GB available

24.8 GB required16.0 GB available

155% VRAM needed

8.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.0 tok/s

TTFT

19314 ms

Safe context

Memory

24.8 GB / 16.0 GB

Offload

40%

Memory breakdown

Weights19.5 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsAya Expanse 32B on RTX 5070 Ti 16GB
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: 10.0 tok/s decode · 19.3s TTFT (warm) · 25 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 10% 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 1.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	11.1 tok/s	9503 ms	4K
Coding	F	Too heavy	10.0 tok/s	19314 ms	4K
Agentic Coding	F	Too heavy	8.3 tok/s	34034 ms	4K
Reasoning	F	Too heavy	10.0 tok/s	22826 ms	4K
RAG	F	Too heavy	8.3 tok/s	42542 ms	4K

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on RTX 5070 Ti 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	F0
Q3_K_S	3	15.7 GB	Low	F0
NVFP4	4	17.9 GB	Medium	F0
Q4_K_M	4	19.5 GB	Medium	F0
Q5_K_M	5	23.0 GB	High	F0
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

Opciones de mejora

Hardware que ejecuta bien Aya Expanse 32B

RTX 3090 24GBOpción económica

24 GB VRAM (+8)936 GB/s (+40)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.24 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 140%.

~$1,499 MSRP

RTX 4090 24GBMejora NVIDIA

24 GB VRAM (+8)1008 GB/s (+112)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.28 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 180%.

~$1,599 MSRP

RTX 5090 32GBMejor relación calidad-precio

32 GB VRAM (+16)1792 GB/s (+896)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.66.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,999 MSRP

Frequently asked questions

Can RTX 5070 Ti 16GB run Aya Expanse 32B?

Yes, RTX 5070 Ti 16GB can run Aya Expanse 32B at Q2_K quantization (Very compromised (needs ~1.2 GB host RAM)). The recommended Q4_K_M requires 24.8 GB which exceeds available memory, but at Q2_K it needs only 17.7 GB. Expected decode speed: 26.4 tok/s.

How much VRAM does Aya Expanse 32B need?

Aya Expanse 32B (32B parameters) requires approximately 24.8 GB at Q4_K_M quantization. On RTX 5070 Ti 16GB, it fits at Q2_K using 17.7 GB.

What is the best quantization for Aya Expanse 32B?

The recommended quantization is Q4_K_M, but on RTX 5070 Ti 16GB the best fitting quantization is Q2_K, which uses 17.7 GB.

What speed will Aya Expanse 32B run at on RTX 5070 Ti 16GB?

On RTX 5070 Ti 16GB, Aya Expanse 32B achieves approximately 26.4 tokens per second decode speed with a time-to-first-token of 7342ms using Q2_K quantization.

Can RTX 5070 Ti 16GB run Aya Expanse 32B for coding?

For coding workloads, Aya Expanse 32B on RTX 5070 Ti 16GB receives a F grade with 10.0 tok/s and 4K context.

What context window can Aya Expanse 32B use on RTX 5070 Ti 16GB?

On RTX 5070 Ti 16GB, Aya Expanse 32B can safely use up to 5K tokens of context at Q2_K quantization. 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 5070 Ti 16GB?

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.

See all results for RTX 5070 Ti 16GB See all hardware for Aya Expanse 32B

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