VOOZH about

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

⇱ BaichuanMed OCR 72B i1 on RTX PRO 6000 Blackwell Server Edi…

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

YES — Runs Great

C52Usable
○Estimated from fit model

BaichuanMed OCR 72B i1 needs ~63.2 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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, 30.5 tok/s, Runs well
63.2 GB required96.0 GB available
66% VRAM used

Fit status

Runs well

Decode

30.5 tok/s

TTFT

6338 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 Server 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: 30.5 tok/s decode · 6.3s TTFT (warm) · 76 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well30.5 tok/s3457 ms78K
CodingCRuns well30.5 tok/s6338 ms78K
Agentic CodingCRuns well30.5 tok/s9220 ms78K
ReasoningCRuns well30.5 tok/s7491 ms78K
RAGCRuns well30.5 tok/s11524 ms78K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
28.1 GB
LowC42
Q3_K_S
3
35.3 GB
LowC43
NVFP4
4
40.3 GB
MediumC45
Q4_K_M
4
43.9 GB
MediumC45
Q5_K_M
5
51.8 GB
HighC47
Q6_K
6
59.0 GB
HighC47
Q8_0Best for your GPU
8
77.0 GB
Very HighC47
F16
16
147.6 GB
MaximumF0

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

Upgrade options

Hardware that runs BaichuanMed OCR 72B i1 well

AMD Instinct MI300A 128GBBudget pick
128 GB VRAM (+32)5300 GB/s (+3703)
C
Raises estimated decode speed by about 177%.84.5 tok/s decode

Raises estimated decode speed by about 177%.

Adds memory headroom for longer context windows and future model growth.

~$12,000 MSRP

Frequently asked questions

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