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URL: https://willitrunai.com/can-run/hf-mradermacher--baichuanmed-ocr-72b-i1-gguf-on-a100-40gb

⇱ BaichuanMed OCR 72B i1 on NVIDIA A100 40GB? No — Alternativ…

Can BaichuanMed OCR 72B i1 run on NVIDIA A100 40GB?

YES — With Q2_K

C49Usable
○Estimated from fit model

BaichuanMed OCR 72B i1 needs ~41.7 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q2_K quantization, expect ~27 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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.

BaichuanMed OCR 72B i1 at Q4_K_M needs 57.6 GB — too much for NVIDIA A100 40GB (40.0 GB). Runs at Q2_K (41.7 GB) with low quality.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 57.6 GB, exceeds 40.0 GB available
57.6 GB required40.0 GB available
144% VRAM needed

17.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.4 tok/s

TTFT

18671 ms

Safe context

4K

Memory

57.6 GB / 40.0 GB

Offload

30%

Memory breakdown

Weights43.9 GB
KV Cache8.4 GB
Runtime1.2 GB
Headroom4.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsBaichuanMed OCR 72B i1 on NVIDIA A100 40GB
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: 10.4 tok/s decode · 18.7s TTFT (warm) · 26 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy12.2 tok/s8676 ms4K
CodingFToo heavy10.4 tok/s18671 ms4K
Agentic CodingFToo heavy7.8 tok/s36221 ms4K
ReasoningFToo heavy10.4 tok/s22066 ms4K
RAGFToo heavy7.8 tok/s45276 ms4K

Quantization options

How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
28.1 GB
LowC47
Q3_K_S
3
35.3 GB
LowF0
NVFP4
4
40.3 GB
MediumF0
Q4_K_M
4
43.9 GB
MediumF0
Q5_K_M
5
51.8 GB
HighF0
Q6_K
6
59.0 GB
HighF0
Q8_0
8
77.0 GB
Very HighF0
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

👁 NVIDIA
NVIDIA A16 64GBBudget pick
64 GB VRAM (+24)
C
Makes the model fit on the accelerator instead of staying completely out of reach.10.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$6,500 MSRP

👁 NVIDIA
RTX PRO 6000 Blackwell Workstation Edition 96GBBest value
96 GB VRAM (+56)1792 GB/s (+237)
C
Makes the model fit on the accelerator instead of staying completely out of reach.34.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$9,999 MSRP

👁 NVIDIA
RTX PRO 6000 Blackwell Server Edition 96GBNVIDIA upgrade
96 GB VRAM (+56)1597 GB/s (+42)
C
Makes the model fit on the accelerator instead of staying completely out of reach.30.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$9,999 MSRP

Frequently asked questions

See all results for NVIDIA A100 40GBSee all hardware for BaichuanMed OCR 72B i1