Can OLMo 2 32B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A80Great

Estimated from fit model

OLMo 2 32B needs ~37.1 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

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) — 37.1 GB, 111.5 tok/s, Runs well

37.1 GB required128.0 GB available

29% VRAM used

Fit status

Runs well

Decode

111.5 tok/s

TTFT

1736 ms

Safe context

Memory

37.1 GB / 128.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsOLMo 2 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: 111.5 tok/s decode · 1.7s TTFT (warm) · 279 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	111.5 tok/s	947 ms	4K
Coding	A	Runs well	111.5 tok/s	1736 ms	4K
Agentic Coding	A	Runs well	111.5 tok/s	2525 ms	4K
Reasoning	A	Runs well	111.5 tok/s	2051 ms	4K
RAG	A	Runs well	111.5 tok/s	3156 ms	4K

Quantization options

How OLMo 2 32B (32B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A71
Q3_K_S	3	15.7 GB	Low	A71
NVFP4	4	17.9 GB	Medium	A71
Q4_K_M	4	19.5 GB	Medium	A71
Q5_K_M	5	23.0 GB	High	A72
Q6_K	6	26.2 GB	High	A72
Q8_0	8	34.2 GB	Very High	A74
F16Best for your GPU	16	65.6 GB	Maximum	A79

Get started

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

Run

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

Your hardware

More models your Intel Data Center GPU Max 1550 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	29.2 tok/s
Qwen 3.5 122B A10B	122B	S	81 tok/s
Qwen 3.6 35B A3B	35B	S	256.2 tok/s
Qwen 3.5 35B A3B	35B	S	278.6 tok/s
Mistral Small 4 119B	119B	S	87.9 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run OLMo 2 32B?

Yes, Intel Data Center GPU Max 1550 128GB can run OLMo 2 32B with a A grade (Runs well). Expected decode speed: 111.5 tok/s.

How much VRAM does OLMo 2 32B need?

OLMo 2 32B (32B parameters) requires approximately 37.1 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 Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, OLMo 2 32B achieves approximately 111.5 tokens per second decode speed with a time-to-first-token of 1736ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run OLMo 2 32B for coding?

For coding workloads, OLMo 2 32B on Intel Data Center GPU Max 1550 128GB receives a A grade with 111.5 tok/s and 4K context.

What context window can OLMo 2 32B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, 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 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 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 Data Center GPU Max 1550 128GB See all hardware for OLMo 2 32B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

111.5 tok/s

947 ms

Coding

Runs well

111.5 tok/s

1736 ms

Agentic Coding

Runs well

111.5 tok/s

2525 ms

Reasoning

Runs well

111.5 tok/s

2051 ms

RAG

Runs well

111.5 tok/s

3156 ms

Quantization options

How OLMo 2 32B (32B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A71
Q3_K_S	3	15.7 GB	Low	A71
NVFP4	4	17.9 GB	Medium	A71
Q4_K_M	4	19.5 GB	Medium	A71
Q5_K_M	5	23.0 GB	High	A72
Q6_K	6	26.2 GB	High	A72
Q8_0	8	34.2 GB	Very High	A74
F16Best for your GPU	16	65.6 GB	Maximum	A79

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

29.2 tok/s

Qwen 3.5 122B A10B

122B

81 tok/s

Qwen 3.6 35B A3B

35B

256.2 tok/s

Qwen 3.5 35B A3B

35B

278.6 tok/s

Mistral Small 4 119B

119B

87.9 tok/s