Will It Run AI

Can Jina Embeddings v3 run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A74Great
Estimated from fit model

Jina Embeddings v3 needs ~17.1 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With F16 quantization, expect ~8 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

F16 (Maximum quality) — 17.1 GB, 8.0 tok/s, Runs well
17.1 GB required128.0 GB available
13% VRAM used

Fit status

Runs well

Decode

8.0 tok/s

TTFT

24176 ms

Safe context

8K

Memory

17.1 GB / 128.0 GB

Memory breakdown

Weights1.2 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsJina Embeddings v3 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: 8.0 tok/s decode · 24.2s TTFT (warm) · 20 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 well8.0 tok/s13187 ms8K
CodingARuns well8.0 tok/s24176 ms8K
Agentic CodingARuns well8.0 tok/s35165 ms8K
ReasoningARuns well8.0 tok/s28571 ms8K
RAGARuns well8.0 tok/s43956 ms8K

Quantization options

How Jina Embeddings v3 (0.5720000267028809B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.2 GB
LowA75
Q3_K_S
3
0.3 GB
LowA75
NVFP4
4
0.3 GB
MediumA75
Q4_K_M
4
0.3 GB
MediumA75
Q5_K_M
5
0.4 GB
HighA75
Q6_K
6
0.5 GB
HighA75
Q8_0
8
0.6 GB
Very HighA75
F16Best for your GPU
16
1.2 GB
MaximumA75

Get started

Copy-paste commands to run Jina Embeddings v3 on your machine.

Run

ollama run jina/jina-embeddings-v3

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 27B27BS132.2 tok/s
AlibabaQwen 3.6 27B27BS132.6 tok/s
AlibabaQwen 3.5 122B A10B122BS81 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Jina Embeddings v3?

Yes, Intel Data Center GPU Max 1550 128GB can run Jina Embeddings v3 with a A grade (Runs well). Expected decode speed: 8.0 tok/s.

How much VRAM does Jina Embeddings v3 need?

Jina Embeddings v3 (0.5720000267028809B parameters) requires approximately 17.1 GB of memory with F16 quantization.

What is the best quantization for Jina Embeddings v3?

The recommended quantization for Jina Embeddings v3 is F16, which balances quality and memory efficiency.

What speed will Jina Embeddings v3 run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Jina Embeddings v3 achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24176ms using F16 quantization.

Can Intel Data Center GPU Max 1550 128GB run Jina Embeddings v3 for coding?

For coding workloads, Jina Embeddings v3 on Intel Data Center GPU Max 1550 128GB receives a A grade with 8.0 tok/s and 8K context.

What context window can Jina Embeddings v3 use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Jina Embeddings v3 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 Jina Embeddings v3 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 Jina Embeddings v3?

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 Jina Embeddings v3
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