Can BaichuanMed OCR 72B i1 run on RTX PRO 6000 Blackwell Workstation Edition 96GB?

YES — Runs Great

C52Usable

○Estimated from fit model

BaichuanMed OCR 72B i1 needs ~63.2 GB VRAM. RTX PRO 6000 Blackwell Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 63.2 GB, 34.3 tok/s, Runs well

63.2 GB required96.0 GB available

66% VRAM used

Fit status

Runs well

Decode

34.3 tok/s

TTFT

5649 ms

Safe context

78K

Memory

63.2 GB / 96.0 GB

Memory breakdown

Weights43.9 GB

KV Cache8.4 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsBaichuanMed OCR 72B i1 on RTX PRO 6000 Blackwell Workstation Edition 96GB
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: 34.3 tok/s decode · 5.6s TTFT (warm) · 86 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	34.3 tok/s	3081 ms	78K
Coding	C	Runs well	34.3 tok/s	5649 ms	78K
Agentic Coding	C	Runs well	34.3 tok/s	8216 ms	78K
Reasoning	C	Runs well	34.3 tok/s	6676 ms	78K
RAG	C	Runs well	34.3 tok/s	10270 ms	78K

Quantization options

How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on RTX PRO 6000 Blackwell Workstation Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	C42
Q3_K_S	3	35.3 GB	Low	C43
NVFP4	4

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

Frequently asked questions

See all results for RTX PRO 6000 Blackwell Workstation Edition 96GB See all hardware for BaichuanMed OCR 72B i1

URL: https://willitrunai.com/can-run/hf-mradermacher--baichuanmed-ocr-72b-i1-gguf-on-rtx-pro-6000-blackwell-96gb

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