Will It Run AI

Can Cerebras-GPT 13B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

B64Good
Estimated from fit model

Cerebras-GPT 13B needs ~33.1 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q5_K_M quantization, expect ~182 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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

Q5_K_M (High quality) — 33.1 GB, 182.0 tok/s, Runs well
33.1 GB required128.0 GB available
26% VRAM used

Fit status

Runs well

Decode

182.0 tok/s

TTFT

1064 ms

Safe context

131K

Memory

33.1 GB / 128.0 GB

Memory breakdown

Weights9.4 GB
KV Cache9.8 GB
Runtime1.2 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsCerebras-GPT 13B 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: 182.0 tok/s decode · 1.1s TTFT (warm) · 455 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 well182.0 tok/s580 ms131K
CodingBRuns well182.0 tok/s1064 ms131K
Agentic CodingBRuns well182.0 tok/s1547 ms131K
ReasoningBRuns well182.0 tok/s1257 ms131K
RAGBRuns well182.0 tok/s1934 ms131K

Quantization options

How Cerebras-GPT 13B (13B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.1 GB
LowC54
Q3_K_S
3
6.4 GB
LowC54
NVFP4
4
7.3 GB
MediumC54
Q4_K_M
4
7.9 GB
MediumC54
Q5_K_M
5
9.4 GB
HighC54
Q6_K
6
10.7 GB
HighC54
Q8_0
8
13.9 GB
Very HighC55
F16Best for your GPU
16
26.7 GB
MaximumB56

Get started

Copy-paste commands to run Cerebras-GPT 13B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "cerebras/Cerebras-GPT-13B" \ --hf-file "Cerebras-GPT-13B-Q5_K_M.gguf" \ -c 4096 -ngl 99

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Cerebras-GPT 13B?

Yes, Intel Data Center GPU Max 1550 128GB can run Cerebras-GPT 13B with a B grade (Runs well). Expected decode speed: 182.0 tok/s.

How much VRAM does Cerebras-GPT 13B need?

Cerebras-GPT 13B (13B parameters) requires approximately 33.1 GB of memory with Q5_K_M quantization.

What is the best quantization for Cerebras-GPT 13B?

The recommended quantization for Cerebras-GPT 13B is Q5_K_M, which balances quality and memory efficiency.

What speed will Cerebras-GPT 13B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Cerebras-GPT 13B achieves approximately 182.0 tokens per second decode speed with a time-to-first-token of 1064ms using Q5_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Cerebras-GPT 13B for coding?

For coding workloads, Cerebras-GPT 13B on Intel Data Center GPU Max 1550 128GB receives a B grade with 182.0 tok/s and 131K context.

What context window can Cerebras-GPT 13B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Cerebras-GPT 13B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Cerebras-GPT 13B 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 Cerebras-GPT 13B?

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 Cerebras-GPT 13B
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