Will It Run AI

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

YES — Runs Great

C50Usable
Estimated from fit model

Yi 34B Chat needs ~38.1 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~106 tok/s.

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

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

200K

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 34B Chat 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: 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well105.5 tok/s1001 ms200K
CodingCRuns well105.5 tok/s1834 ms200K
Agentic CodingCRuns well105.5 tok/s2668 ms200K
ReasoningCRuns well105.5 tok/s2168 ms200K
RAGCRuns well105.5 tok/s3335 ms200K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
13.3 GB
LowC40
Q3_K_S
3
16.7 GB
LowC40
NVFP4
4
19.0 GB
MediumC41
Q4_K_M
4
20.7 GB
MediumC41
Q5_K_M
5
24.5 GB
HighC41
Q6_K
6
27.9 GB
HighC42
Q8_0
8
36.4 GB
Very HighC43
F16Best for your GPU
16
69.7 GB
MaximumC49

Get started

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

Run

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

Frequently asked questions

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

Yes, Intel Data Center GPU Max 1550 128GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 105.5 tok/s.

How much VRAM does Yi 34B Chat need?

Yi 34B Chat (34B parameters) requires approximately 38.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 34B Chat?

The recommended quantization for Yi 34B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 34B Chat run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Yi 34B Chat achieves approximately 105.5 tokens per second decode speed with a time-to-first-token of 1834ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Yi 34B Chat for coding?

For coding workloads, Yi 34B Chat on Intel Data Center GPU Max 1550 128GB receives a C grade with 105.5 tok/s and 200K context.

What context window can Yi 34B Chat use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Yi 34B Chat can safely use up to 200K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.

What should I upgrade first if Yi 34B Chat 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 Yi 34B Chat?

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 Yi 34B Chat
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