Can BaichuanMed OCR 72B i1 run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

C53Usable

Estimated from fit model

BaichuanMed OCR 72B i1 needs ~61.6 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~38 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) — 61.6 GB, 38.3 tok/s, Runs well

61.6 GB required80.0 GB available

77% VRAM used

Fit status

Runs well

Decode

38.3 tok/s

TTFT

5061 ms

Safe context

51K

Memory

61.6 GB / 80.0 GB

Memory breakdown

Weights43.9 GB

KV Cache8.4 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsBaichuanMed OCR 72B i1 on NVIDIA H100 PCIe 80GB
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: 38.3 tok/s decode · 5.1s TTFT (warm) · 96 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	38.3 tok/s	2761 ms	51K
Coding	C	Runs well	38.3 tok/s	5061 ms	51K
Agentic Coding	C	Tight fit	38.3 tok/s	7362 ms	51K
Reasoning	C	Runs well	38.3 tok/s	5981 ms	51K
RAG	C	Tight fit	38.3 tok/s	9202 ms	51K

Quantization options

How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	C43
Q3_K_S	3	35.3 GB	Low	C45
NVFP4	4	40.3 GB	Medium	C47
Q4_K_M	4	43.9 GB	Medium	C47
Q5_K_M	5	51.8 GB	High	C47
Q6_KBest for your GPU	6	59.0 GB	High	C47
Q8_0	8	77.0 GB	Very High	F0
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

升级选项

能流畅运行 BaichuanMed OCR 72B i1 的硬件

NVIDIA H20 96GB经济之选

96 GB VRAM (+16)4000 GB/s (+2000)

Raises estimated decode speed by about 93%.73.8 tok/s 解码

Raises estimated decode speed by about 93%.

~$12,000 MSRP

NVIDIA GH200 96GB最佳性价比

96 GB VRAM (+16)4000 GB/s (+2000)

Raises estimated decode speed by about 93%.73.8 tok/s 解码

Raises estimated decode speed by about 93%.

~$30,000 MSRP

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run BaichuanMed OCR 72B i1?

Yes, NVIDIA H100 PCIe 80GB can run BaichuanMed OCR 72B i1 with a C grade (Runs well). Expected decode speed: 38.3 tok/s.

How much VRAM does BaichuanMed OCR 72B i1 need?

BaichuanMed OCR 72B i1 (72B parameters) requires approximately 61.6 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 NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, BaichuanMed OCR 72B i1 achieves approximately 38.3 tokens per second decode speed with a time-to-first-token of 5061ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run BaichuanMed OCR 72B i1 for coding?

For coding workloads, BaichuanMed OCR 72B i1 on NVIDIA H100 PCIe 80GB receives a C grade with 38.3 tok/s and 51K context.

What context window can BaichuanMed OCR 72B i1 use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, BaichuanMed OCR 72B i1 can safely use up to 51K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H100 PCIe 80GB See all hardware for BaichuanMed OCR 72B i1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-mradermacher--baichuanmed-ocr-72b-i1-gguf-on-h100-pcie-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: