Will It Run AI

Can Aya Expanse 32B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

C53Usable
Estimated from fit model

Aya Expanse 32B needs ~35.7 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 35.7 GB, 112.3 tok/s, Runs well
35.7 GB required128.0 GB available
28% VRAM used

Fit status

Runs well

Decode

112.3 tok/s

TTFT

1724 ms

Safe context

8K

Memory

35.7 GB / 128.0 GB

Memory breakdown

Weights19.5 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsAya Expanse 32B on Intel Data Center GPU Max 1550 128GB
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: 112.3 tok/s decode · 1.7s TTFT (warm) · 281 tok/s prefill

What limits this setup

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

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.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well112.3 tok/s940 ms8K
CodingCRuns well112.3 tok/s1724 ms8K
Agentic CodingCRuns well112.3 tok/s2507 ms8K
ReasoningCRuns well112.3 tok/s2037 ms8K
RAGCRuns well103.3 tok/s3408 ms8K

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowC44
Q3_K_S
3
15.7 GB
LowC44
NVFP4
4
17.9 GB
MediumC44
Q4_K_M
4
19.5 GB
MediumC44
Q5_K_M
5
23.0 GB
HighC45
Q6_K
6
26.2 GB
HighC45
Q8_0
8
34.2 GB
Very HighC47
F16Best for your GPU
16
65.6 GB
MaximumC52

Get started

Copy-paste commands to run Aya Expanse 32B on your machine.

Run

ollama run aya-expanse:32b

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Aya Expanse 32B?

Yes, Intel Data Center GPU Max 1550 128GB can run Aya Expanse 32B with a C grade (Runs well). Expected decode speed: 112.3 tok/s.

How much VRAM does Aya Expanse 32B need?

Aya Expanse 32B (32B parameters) requires approximately 35.7 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 Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Aya Expanse 32B achieves approximately 112.3 tokens per second decode speed with a time-to-first-token of 1724ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Aya Expanse 32B for coding?

For coding workloads, Aya Expanse 32B on Intel Data Center GPU Max 1550 128GB receives a C grade with 112.3 tok/s and 8K context.

What context window can Aya Expanse 32B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, 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 Data Center GPU Max 1550 128GB?

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.

Would CUDA be a better path than Intel Data Center GPU Max 1550 128GB 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.

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