Will It Run AI

Can MPT-30B-Instruct run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A72Great
Estimated from fit model

MPT-30B-Instruct needs ~59.0 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q5_K_M quantization, expect ~95 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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) — 59.0 GB, 95.2 tok/s, Runs well
59.0 GB required128.0 GB available
46% VRAM used

Fit status

Runs well

Decode

95.2 tok/s

TTFT

2034 ms

Safe context

8K

Memory

59.0 GB / 128.0 GB

Memory breakdown

Weights21.6 GB
KV Cache23.4 GB
Runtime1.2 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsMPT-30B-Instruct 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: 95.2 tok/s decode · 2.0s TTFT (warm) · 238 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
ChatARuns well95.2 tok/s1109 ms8K
CodingARuns well95.2 tok/s2034 ms8K
Agentic CodingARuns well95.2 tok/s2958 ms8K
ReasoningARuns well95.2 tok/s2403 ms8K
RAGARuns well95.2 tok/s3697 ms8K

Quantization options

How MPT-30B-Instruct (30B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
11.7 GB
LowB59
Q3_K_S
3
14.7 GB
LowB59
NVFP4
4
16.8 GB
MediumB59
Q4_K_M
4
18.3 GB
MediumB60
Q5_K_M
5
21.6 GB
HighB60
Q6_K
6
24.6 GB
HighB60
Q8_0
8
32.1 GB
Very HighB61
F16Best for your GPU
16
61.5 GB
MaximumB66

Get started

Copy-paste commands to run MPT-30B-Instruct on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "mosaicml/mpt-30b-instruct" \ --hf-file "mpt-30b-instruct-Q5_K_M.gguf" \ -c 4096 -ngl 99

Your hardware

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

ModelParamsGradeDecodeCapabilities
MistralDevstral 2 123B Instruct123BS29.2 tok/s
AlibabaQwen3-Coder 30B A3B Instruct30.5BS304.8 tok/s
AlibabaQwen 3.5 122B A10B122BS81 tok/s
AlibabaQwen 3.6 35B A3B35BS256.2 tok/s
AlibabaQwen 3.5 35B A3B35BS278.6 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run MPT-30B-Instruct?

Yes, Intel Data Center GPU Max 1550 128GB can run MPT-30B-Instruct with a A grade (Runs well). Expected decode speed: 95.2 tok/s.

How much VRAM does MPT-30B-Instruct need?

MPT-30B-Instruct (30B parameters) requires approximately 59.0 GB of memory with Q5_K_M quantization.

What is the best quantization for MPT-30B-Instruct?

The recommended quantization for MPT-30B-Instruct is Q5_K_M, which balances quality and memory efficiency.

What speed will MPT-30B-Instruct run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, MPT-30B-Instruct achieves approximately 95.2 tokens per second decode speed with a time-to-first-token of 2034ms using Q5_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run MPT-30B-Instruct for coding?

For coding workloads, MPT-30B-Instruct on Intel Data Center GPU Max 1550 128GB receives a A grade with 95.2 tok/s and 8K context.

What context window can MPT-30B-Instruct use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, MPT-30B-Instruct 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 MPT-30B-Instruct 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 MPT-30B-Instruct?

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 MPT-30B-Instruct
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/mpt-30b-instruct-on-max-1550-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: