Can Yi 1.5 34B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

B61Good

○Estimated from fit model

Yi 1.5 34B needs ~38.1 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~97 tok/s.

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

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) — 38.1 GB, 105.5 tok/s, Runs well

38.1 GB required128.0 GB available

30% VRAM used

Fit status

Runs well

Decode

105.5 tok/s

TTFT

1834 ms

Safe context

Memory

38.1 GB / 128.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsYi 1.5 34B 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: 105.5 tok/s decode · 1.8s TTFT (warm) · 264 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	105.5 tok/s	1001 ms	4K
Coding	B	Runs well	97.2 tok/s	1992 ms	4K
Agentic Coding	B	Runs well	105.5 tok/s	2668 ms	4K
Reasoning	B	Runs well	105.5 tok/s	2168 ms	4K
RAG	B	Runs well	105.5 tok/s	3335 ms	4K

Quantization options

How Yi 1.5 34B (34B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	C51
Q3_K_S	3	16.7 GB	Low	C51
NVFP4	4

Get started

Copy-paste commands to run Yi 1.5 34B on your machine.

Run

lms load Yi-1.5-34B-Chat && lms server start

Frequently asked questions

See all results for Intel Data Center GPU Max 1550 128GB See all hardware for Yi 1.5 34B

URL: https://willitrunai.com/can-run/yi-1.5-34b-on-max-1550-128gb

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