Can BaichuanMed OCR 72B i1 run on NVIDIA L20 48GB?

YES — With NVFP4

D36Poor

Estimated from fit model

BaichuanMed OCR 72B i1 needs ~54.8 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With NVFP4 quantization, expect ~9 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Host offload

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 58.4 GB — too much for NVIDIA L20 48GB (48.0 GB). Runs at NVFP4 (54.8 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 58.4 GB, exceeds 48.0 GB available

58.4 GB required48.0 GB available

122% VRAM needed

10.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.1 tok/s

TTFT

27120 ms

Safe context

Memory

58.4 GB / 48.0 GB

Offload

20%

Memory breakdown

Weights43.9 GB

KV Cache8.4 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsBaichuanMed OCR 72B i1 on NVIDIA L20 48GB
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: 7.1 tok/s decode · 27.1s TTFT (warm) · 18 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 5.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	D	Very compromised (needs ~5 GB host RAM)	8.4 tok/s	12631 ms	4K
Coding	F	Too heavy	7.1 tok/s	27120 ms	4K
Agentic Coding	F	Too heavy	5.4 tok/s	52417 ms	4K
Reasoning	F	Too heavy	7.1 tok/s	32051 ms	4K
RAG	F	Too heavy	5.4 tok/s	65521 ms	4K

Quantization options

How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	C47
Q3_K_SBest for your GPU	3	35.3 GB	Low	C47
NVFP4	4	40.3 GB	Medium	F0
Q4_K_M	4	43.9 GB	Medium	F0
Q5_K_M	5	51.8 GB	High	F0
Q6_K	6	59.0 GB	High	F0
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

Opciones de mejora

Hardware que ejecuta bien BaichuanMed OCR 72B i1

NVIDIA A16 64GBOpción económica

64 GB VRAM (+16)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.10.7 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$6,500 MSRP

RTX PRO 6000 Blackwell Workstation Edition 96GBMejor relación calidad-precio

96 GB VRAM (+48)1792 GB/s (+928)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.34.3 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBMejora NVIDIA

96 GB VRAM (+48)1597 GB/s (+733)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.30.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$9,999 MSRP

Frequently asked questions

Can NVIDIA L20 48GB run BaichuanMed OCR 72B i1?

Yes, NVIDIA L20 48GB can run BaichuanMed OCR 72B i1 at NVFP4 quantization (Very compromised (needs ~5 GB host RAM)). The recommended Q4_K_M requires 58.4 GB which exceeds available memory, but at NVFP4 it needs only 54.8 GB. Expected decode speed: 9.3 tok/s.

How much VRAM does BaichuanMed OCR 72B i1 need?

BaichuanMed OCR 72B i1 (72B parameters) requires approximately 58.4 GB at Q4_K_M quantization. On NVIDIA L20 48GB, it fits at NVFP4 using 54.8 GB.

What is the best quantization for BaichuanMed OCR 72B i1?

The recommended quantization is Q4_K_M, but on NVIDIA L20 48GB the best fitting quantization is NVFP4, which uses 54.8 GB.

What speed will BaichuanMed OCR 72B i1 run at on NVIDIA L20 48GB?

On NVIDIA L20 48GB, BaichuanMed OCR 72B i1 achieves approximately 9.3 tokens per second decode speed with a time-to-first-token of 20737ms using NVFP4 quantization.

Can NVIDIA L20 48GB run BaichuanMed OCR 72B i1 for coding?

For coding workloads, BaichuanMed OCR 72B i1 on NVIDIA L20 48GB receives a F grade with 7.1 tok/s and 4K context.

What context window can BaichuanMed OCR 72B i1 use on NVIDIA L20 48GB?

On NVIDIA L20 48GB, BaichuanMed OCR 72B i1 can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if BaichuanMed OCR 72B i1 feels slow on NVIDIA L20 48GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for NVIDIA L20 48GB See all hardware for BaichuanMed OCR 72B i1

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