Would CUDA be a better path than Intel Arc Pro B60 24GB for Aya Expanse 32B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

Can Aya Expanse 32B run on Intel Arc Pro B60 24GB?

Q: What should I upgrade first if Aya Expanse 32B feels slow on Intel Arc Pro B60 24GB?

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.

YES — With Offload

C52Usable

Estimated from fit model

Aya Expanse 32B needs ~25.3 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 25.3 GB, 9.4 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

9.4 tok/s

TTFT

20487 ms

Safe context

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 Intel Arc Pro B60 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: 9.4 tok/s decode · 20.5s TTFT (warm) · 24 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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

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	C	Runs with offload (needs ~0 GB host RAM)	10.5 tok/s	10101 ms	8K
Coding	C	Runs with offload (needs ~1 GB host RAM)	9.4 tok/s	20487 ms	8K
Agentic Coding	C	Very compromised (needs ~2.6 GB host RAM)	7.8 tok/s	35971 ms	8K
Reasoning	C	Runs with offload (needs ~1 GB host RAM)	9.4 tok/s	24212 ms	8K
RAG	C	Very compromised (needs ~2.6 GB host RAM)	7.8 tok/s

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	B56
Q3_K_S	3	15.7 GB	Low	B55
NVFP4Best for your GPU

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 A100 40GBBiggest leap

40 GB VRAM (+16)1555 GB/s (+1099)

Raises estimated decode speed by about 674%.72.8 tok/s decode

Raises estimated decode speed by about 674%.

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

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$10,000 MSRP

Intel Data Center GPU Max 1550 128GBBudget pick

128 GB VRAM (+104)3200 GB/s (+2744)

Raises estimated decode speed by about 1095%.112.3 tok/s decode

Raises estimated decode speed by about 1095%.

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

~$15,000 MSRP

Gaudi 3 128GBBest value

128 GB VRAM (+104)3700 GB/s (+3244)

Raises estimated decode speed by about 1435%.144.3 tok/s decode

Raises estimated decode speed by about 1435%.

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

~$15,000 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Aya Expanse 32B?

Yes, Intel Arc Pro B60 24GB can run Aya Expanse 32B with a C grade (Runs with offload (needs ~1 GB host RAM)). Expected decode speed: 9.4 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 Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Aya Expanse 32B achieves approximately 9.4 tokens per second decode speed with a time-to-first-token of 20487ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Aya Expanse 32B for coding?

For coding workloads, Aya Expanse 32B on Intel Arc Pro B60 24GB receives a C grade with 9.4 tok/s and 8K context.

What context window can Aya Expanse 32B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 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 Intel Arc Pro B60 24GB?

See all results for Intel Arc Pro B60 24GB See all hardware for Aya Expanse 32B

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