Can BaichuanMed OCR 72B i1 run on B100 192GB?

YES — Runs Great

C49Usable

○Estimated from fit model

BaichuanMed OCR 72B i1 needs ~72.8 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~153 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) — 72.8 GB, 153.0 tok/s, Runs well

72.8 GB required192.0 GB available

38% VRAM used

Fit status

Runs well

Decode

153.0 tok/s

TTFT

1265 ms

Safe context

242K

Memory

72.8 GB / 192.0 GB

Memory breakdown

Weights43.9 GB

KV Cache8.4 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsBaichuanMed OCR 72B i1 on B100 192GB
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: 153.0 tok/s decode · 1.3s TTFT (warm) · 383 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	153.0 tok/s	690 ms	242K
Coding	C	Runs well	153.0 tok/s	1265 ms	242K
Agentic Coding	C	Runs well	153.0 tok/s	1840 ms	242K
Reasoning	C	Runs well	153.0 tok/s	1495 ms	242K
RAG	C	Runs well	153.0 tok/s	2301 ms	242K

Quantization options

How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	D38
Q3_K_S	3	35.3 GB	Low	D39
NVFP4	4	40.3 GB	Medium	D39
Q4_K_M	4	43.9 GB	Medium	D40
Q5_K_M	5	51.8 GB	High	C41
Q6_K	6	59.0 GB	High	C42
Q8_0	8	77.0 GB	Very High	C44
F16Best for your GPU	16	147.6 GB	Maximum	C47

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 B100 192GB See all hardware for BaichuanMed OCR 72B i1

URL: https://willitrunai.com/can-run/hf-mradermacher--baichuanmed-ocr-72b-i1-gguf-on-b100-192gb

⇱ BaichuanMed OCR 72B i1 on B100 192GB? YES