Will It Run AI

Can Gemma 2 27B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

B68Good
Estimated from fit model

Gemma 2 27B needs ~41.4 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~80 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) — 41.4 GB, 79.9 tok/s, Runs well
41.4 GB required128.0 GB available
32% VRAM used

Fit status

Runs well

Decode

79.9 tok/s

TTFT

2424 ms

Safe context

8K

Memory

41.4 GB / 128.0 GB

Memory breakdown

Weights16.5 GB
KV Cache11.2 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsGemma 2 27B on Intel Data Center GPU Max 1550 128GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 79.9 tok/s decode · 2.4s TTFT (warm) · 200 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
ChatBRuns well79.9 tok/s1322 ms8K
CodingBRuns well79.9 tok/s2424 ms8K
Agentic CodingBRuns well79.9 tok/s3526 ms8K
ReasoningBRuns well79.9 tok/s2865 ms8K
RAGBRuns well79.9 tok/s4407 ms8K

Quantization options

How Gemma 2 27B (27B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
10.5 GB
LowB58
Q3_K_S
3
13.2 GB
LowB58
NVFP4
4
15.1 GB
MediumB58
Q4_K_M
4
16.5 GB
MediumB58
Q5_K_M
5
19.4 GB
HighB59
Q6_K
6
22.1 GB
HighB59
Q8_0
8
28.9 GB
Very HighB60
F16Best for your GPU
16
55.4 GB
MaximumB64

Get started

Copy-paste commands to run Gemma 2 27B on your machine.

Run

ollama run gemma2:27b

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Gemma 2 27B?

Yes, Intel Data Center GPU Max 1550 128GB can run Gemma 2 27B with a B grade (Runs well). Expected decode speed: 79.9 tok/s.

How much VRAM does Gemma 2 27B need?

Gemma 2 27B (27B parameters) requires approximately 41.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 2 27B?

The recommended quantization for Gemma 2 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 2 27B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Gemma 2 27B achieves approximately 79.9 tokens per second decode speed with a time-to-first-token of 2424ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Gemma 2 27B for coding?

For coding workloads, Gemma 2 27B on Intel Data Center GPU Max 1550 128GB receives a B grade with 79.9 tok/s and 8K context.

What context window can Gemma 2 27B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Gemma 2 27B 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 Gemma 2 27B 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 Gemma 2 27B?

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 128GBSee all hardware for Gemma 2 27B
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