Will It Run AI

Can Llama 4 Scout 17B 16E run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A83Great
Estimated from fit model

Llama 4 Scout 17B 16E needs ~83.1 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~77 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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

Q4_K_M (Medium quality) — 83.1 GB, 77.1 tok/s, Runs well
83.1 GB required128.0 GB available
65% VRAM used

Fit status

Runs well

Decode

77.1 tok/s

TTFT

2511 ms

Safe context

261K

Memory

83.1 GB / 128.0 GB

Memory breakdown

Weights66.5 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsLlama 4 Scout 17B 16E 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: 77.1 tok/s decode · 2.5s TTFT (warm) · 193 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 well77.1 tok/s1370 ms261K
CodingARuns well77.1 tok/s2511 ms261K
Agentic CodingARuns well77.1 tok/s3653 ms261K
ReasoningARuns well77.1 tok/s2968 ms261K
RAGARuns well77.1 tok/s4566 ms261K

Quantization options

How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
42.5 GB
LowA71
Q3_K_S
3
53.4 GB
LowA73
NVFP4
4
61.0 GB
MediumA74
Q4_K_M
4
66.5 GB
MediumA75
Q5_K_M
5
78.5 GB
HighA76
Q6_KBest for your GPU
6
89.4 GB
HighA76
Q8_0
8
116.6 GB
Very HighF0
F16
16
223.5 GB
MaximumF0

Get started

Copy-paste commands to run Llama 4 Scout 17B 16E on your machine.

Run

lms load Llama-4-Scout-17B-16E-Instruct && lms server start

Your hardware

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

ModelParamsGradeDecodeCapabilities
MistralDevstral 2 123B Instruct123BS29.2 tok/s
AlibabaQwen 3.5 122B A10B122BS81 tok/s
MistralMistral Small 4 119B119BS87.9 tok/s
OpenAIGPT-OSS 120B117BS30.7 tok/s
CohereCommand A 111B111BS32.5 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Llama 4 Scout 17B 16E?

Yes, Intel Data Center GPU Max 1550 128GB can run Llama 4 Scout 17B 16E with a A grade (Runs well). Expected decode speed: 77.1 tok/s.

How much VRAM does Llama 4 Scout 17B 16E need?

Llama 4 Scout 17B 16E (109B parameters) requires approximately 83.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 4 Scout 17B 16E?

The recommended quantization for Llama 4 Scout 17B 16E is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 4 Scout 17B 16E run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Llama 4 Scout 17B 16E achieves approximately 77.1 tokens per second decode speed with a time-to-first-token of 2511ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Llama 4 Scout 17B 16E for coding?

For coding workloads, Llama 4 Scout 17B 16E on Intel Data Center GPU Max 1550 128GB receives a A grade with 77.1 tok/s and 261K context.

What context window can Llama 4 Scout 17B 16E use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Llama 4 Scout 17B 16E can safely use up to 261K tokens of context. The model's official context limit is 10.5M, but available memory constrains the safe maximum.

What should I upgrade first if Llama 4 Scout 17B 16E 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 Llama 4 Scout 17B 16E?

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 Llama 4 Scout 17B 16E
Embed this result▼

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

<iframe src="https://willitrunai.com/embed/llama-4-scout-17b-16e-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: