Can Aya Expanse 32B run on Intel Arc A770 16GB?

YES — With Q2_K

C43Usable

○Estimated from fit model

Aya Expanse 32B needs ~17.4 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q2_K quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: 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 24.5 GB — too much for Intel Arc A770 16GB (16.0 GB). Runs at Q2_K (17.4 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 24.5 GB, exceeds 16.0 GB available

24.5 GB required16.0 GB available

153% VRAM needed

8.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.3 tok/s

TTFT

44934 ms

Safe context

Memory

24.5 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights19.5 GB

KV Cache2.4 GB

Runtime0.9 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 Intel Arc A770 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: 4.3 tok/s decode · 44.9s TTFT (warm) · 11 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.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

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.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	4.8 tok/s	22006 ms	4K
Coding	F	Too heavy	4.3 tok/s	44934 ms	4K
Agentic Coding	F	Too heavy	3.5 tok/s	79850 ms	4K
Reasoning	F	Too heavy	4.3 tok/s	53104 ms	4K
RAG	F	Too heavy	3.5 tok/s	99812 ms	4K

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on Intel Arc A770 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

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

👁 Intel
Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.9.4 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.

~$599 MSRP

👁 NVIDIA
NVIDIA A100 40GBBiggest leap

40 GB VRAM (+24)1555 GB/s (+995)

Makes the model fit on the accelerator instead of staying completely out of reach.72.8 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.

~$10,000 MSRP

👁 Intel
Intel Data Center GPU Max 1550 128GBBest value

128 GB VRAM (+112)3200 GB/s (+2640)

Makes the model fit on the accelerator instead of staying completely out of reach.112.3 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.

~$15,000 MSRP

👁 Intel
Gaudi 3 128GBIntel upgrade

128 GB VRAM (+112)3700 GB/s (+3140)

Makes the model fit on the accelerator instead of staying completely out of reach.144.3 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.

~$15,000 MSRP

Frequently asked questions

See all results for Intel Arc A770 16GB See all hardware for Aya Expanse 32B

URL: https://willitrunai.com/can-run/aya-expanse-32b-on-arc-a770-16gb

⇱

Can Aya Expanse 32B run on Intel Arc A770 16GB?

YES — With Q2_K

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Aya Expanse 32B well

Frequently asked questions