Will It Run AI

Can OLMo 2 32B run on Intel Arc Pro B60 24GB?

BARELY — Tight on Memory

B68Good
Estimated from fit model

OLMo 2 32B needs ~26.7 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
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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) — 26.7 GB, 8.4 tok/s, Very compromised (needs ~2 GB host RAM)
26.7 GB required24.0 GB available
111% VRAM needed

2.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2 GB host RAM)

Decode

8.4 tok/s

TTFT

23144 ms

Safe context

4K

Memory

26.7 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights19.5 GB
KV Cache3.9 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsOLMo 2 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: 8.4 tok/s decode · 23.1s TTFT (warm) · 21 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns with offload (needs ~0.6 GB host RAM)9.8 tok/s10813 ms4K
CodingBVery compromised7.7 tok/s24995 ms4K
Agentic CodingFToo heavy6.3 tok/s44465 ms4K
ReasoningBVery compromised (needs ~2 GB host RAM)8.4 tok/s27352 ms4K
RAGFToo heavy6.3 tok/s55581 ms4K

Quantization options

How OLMo 2 32B (32B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowA83
Q3_K_S
3
15.7 GB
LowA82
NVFP4Best for your GPU
4
17.9 GB
MediumA82
Q4_K_M
4
19.5 GB
MediumF0
Q5_K_M
5
23.0 GB
HighF0
Q6_K
6
26.2 GB
HighF0
Q8_0
8
34.2 GB
Very HighF0
F16
16
65.6 GB
MaximumF0

Get started

Copy-paste commands to run OLMo 2 32B on your machine.

Run

lms load OLMo-2-0325-32B-Instruct && lms server start

升级选项

能流畅运行 OLMo 2 32B 的硬件

NVIDIANVIDIA A100 40GB最大飞跃
40 GB VRAM (+16)1555 GB/s (+1099)
S
Removes host-memory offload, which is usually the single biggest latency and throughput win.72.3 tok/s 解码

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 761%.

~$10,000 MSRP

IntelIntel Data Center GPU Max 1550 128GB经济之选
128 GB VRAM (+104)3200 GB/s (+2744)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.111.5 tok/s 解码

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 1227%.

~$15,000 MSRP

IntelGaudi 3 128GB最佳性价比
128 GB VRAM (+104)3700 GB/s (+3244)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.143.3 tok/s 解码

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 1606%.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run OLMo 2 32B?

Yes, Intel Arc Pro B60 24GB can run OLMo 2 32B with a B grade (Very compromised). Expected decode speed: 7.7 tok/s.

How much VRAM does OLMo 2 32B need?

OLMo 2 32B (32B parameters) requires approximately 26.7 GB of memory with Q4_K_M quantization.

What is the best quantization for OLMo 2 32B?

The recommended quantization for OLMo 2 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will OLMo 2 32B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, OLMo 2 32B achieves approximately 7.7 tokens per second decode speed with a time-to-first-token of 24995ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run OLMo 2 32B for coding?

For coding workloads, OLMo 2 32B on Intel Arc Pro B60 24GB receives a B grade with 7.7 tok/s and 4K context.

What context window can OLMo 2 32B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, OLMo 2 32B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

What should I upgrade first if OLMo 2 32B feels slow on Intel Arc Pro B60 24GB?

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.

Would CUDA be a better path than Intel Arc Pro B60 24GB for OLMo 2 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.

See all results for Intel Arc Pro B60 24GBSee all hardware for OLMo 2 32B
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