Can BaichuanMed OCR 72B i1 run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

C50Usable

Estimated from fit model

BaichuanMed OCR 72B i1 needs ~66.1 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~46 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) — 66.1 GB, 45.9 tok/s, Runs well

66.1 GB required128.0 GB available

52% VRAM used

Fit status

Runs well

Decode

45.9 tok/s

TTFT

4218 ms

Safe context

133K

Memory

66.1 GB / 128.0 GB

Memory breakdown

Weights43.9 GB

KV Cache8.4 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsBaichuanMed OCR 72B i1 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: 45.9 tok/s decode · 4.2s TTFT (warm) · 115 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	C	Runs well	45.9 tok/s	2301 ms	133K
Coding	C	Runs well	45.9 tok/s	4218 ms	133K
Agentic Coding	C	Runs well	45.9 tok/s	6135 ms	133K
Reasoning	C	Runs well	45.9 tok/s	4985 ms	133K
RAG	C	Runs well	45.9 tok/s	7669 ms	133K

Quantization options

How BaichuanMed OCR 72B i1 (72B 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	28.1 GB	Low	D40
Q3_K_S	3	35.3 GB	Low	C41
NVFP4	4	40.3 GB	Medium	C42
Q4_K_M	4	43.9 GB	Medium	C43
Q5_K_M	5	51.8 GB	High	C44
Q6_K	6	59.0 GB	High	C45
Q8_0Best for your GPU	8	77.0 GB	Very High	C47
F16	16	147.6 GB	Maximum	F0

Get started

Copy-paste commands to run BaichuanMed OCR 72B i1 on your machine.

Run

lms load hf-mradermacher--baichuanmed-ocr-72b-i1-gguf && lms server start

アップグレードオプション

BaichuanMed OCR 72B i1を快適に動かすハードウェア

NVIDIA H200 141GBコスパ重視

141 GB VRAM (+13)4800 GB/s (+1600)

Raises estimated decode speed by about 100%.91.8 tok/sデコード

Raises estimated decode speed by about 100%.

Moves you onto CUDA, which still has the broadest local-AI runtime coverage.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

〜$30,000 MSRP

NVIDIA H200 PCIe 141GBコスパ最良

141 GB VRAM (+13)4800 GB/s (+1600)

Raises estimated decode speed by about 100%.91.8 tok/sデコード

Raises estimated decode speed by about 100%.

Moves you onto CUDA, which still has the broadest local-AI runtime coverage.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

〜$30,000 MSRP

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run BaichuanMed OCR 72B i1?

Yes, Intel Data Center GPU Max 1550 128GB can run BaichuanMed OCR 72B i1 with a C grade (Runs well). Expected decode speed: 45.9 tok/s.

How much VRAM does BaichuanMed OCR 72B i1 need?

BaichuanMed OCR 72B i1 (72B parameters) requires approximately 66.1 GB of memory with Q4_K_M quantization.

What is the best quantization for BaichuanMed OCR 72B i1?

The recommended quantization for BaichuanMed OCR 72B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will BaichuanMed OCR 72B i1 run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, BaichuanMed OCR 72B i1 achieves approximately 45.9 tokens per second decode speed with a time-to-first-token of 4218ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run BaichuanMed OCR 72B i1 for coding?

For coding workloads, BaichuanMed OCR 72B i1 on Intel Data Center GPU Max 1550 128GB receives a C grade with 45.9 tok/s and 133K context.

What context window can BaichuanMed OCR 72B i1 use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, BaichuanMed OCR 72B i1 can safely use up to 133K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if BaichuanMed OCR 72B i1 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 BaichuanMed OCR 72B i1?

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.

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