Will It Run AI

Can BaichuanMed OCR 72B i1 run on NVIDIA A16 64GB?

YES — Tight Fit

C46Usable
Estimated from fit model

BaichuanMed OCR 72B i1 needs ~60.0 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~11 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
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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) — 60.0 GB, 10.7 tok/s, Tight fit
60.0 GB required64.0 GB available
94% VRAM used

Fit status

Tight fit

Decode

10.7 tok/s

TTFT

18169 ms

Safe context

24K

Memory

60.0 GB / 64.0 GB

Memory breakdown

Weights43.9 GB
KV Cache8.4 GB
Runtime1.2 GB
Headroom6.4 GB

See how fast it feels

See how fast it feelsBaichuanMed OCR 72B i1 on NVIDIA A16 64GB
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: 10.7 tok/s decode · 18.2s TTFT (warm) · 27 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
ChatCTight fit10.7 tok/s9910 ms24K
CodingCTight fit10.7 tok/s18169 ms24K
Agentic CodingDRuns with offload (needs ~2.8 GB host RAM)6.9 tok/s40523 ms24K
ReasoningCTight fit10.7 tok/s21472 ms24K
RAGDRuns with offload (needs ~2.8 GB host RAM)6.9 tok/s50654 ms24K

Quantization options

How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
28.1 GB
LowC46
Q3_K_S
3
35.3 GB
LowC47
NVFP4
4
40.3 GB
MediumC47
Q4_K_M
4
43.9 GB
MediumC47
Q5_K_MBest for your GPU
5
51.8 GB
HighC47
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-Optionen

Hardware, die BaichuanMed OCR 72B i1 gut ausführt

NVIDIARTX PRO 6000 Blackwell Workstation Edition 96GBGünstige Wahl
96 GB VRAM (+32)1792 GB/s (+1192)
C
Raises estimated decode speed by about 221%.34.3 tok/s Dekodierung

Raises estimated decode speed by about 221%.

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

ca. $9,999 MSRP

NVIDIARTX PRO 6000 Blackwell Server Edition 96GBBestes Preis-Leistungs-Verhältnis
96 GB VRAM (+32)1597 GB/s (+997)
C
Raises estimated decode speed by about 185%.30.5 tok/s Dekodierung

Raises estimated decode speed by about 185%.

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

ca. $9,999 MSRP

NVIDIANVIDIA H20 96GBNVIDIA-Upgrade
96 GB VRAM (+32)4000 GB/s (+3400)
C
Raises estimated decode speed by about 590%.73.8 tok/s Dekodierung

Raises estimated decode speed by about 590%.

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

ca. $12,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run BaichuanMed OCR 72B i1?

Yes, NVIDIA A16 64GB can run BaichuanMed OCR 72B i1 with a C grade (Tight fit). Expected decode speed: 10.7 tok/s.

How much VRAM does BaichuanMed OCR 72B i1 need?

BaichuanMed OCR 72B i1 (72B parameters) requires approximately 60.0 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 A16 64GB?

On NVIDIA A16 64GB, BaichuanMed OCR 72B i1 achieves approximately 10.7 tokens per second decode speed with a time-to-first-token of 18169ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run BaichuanMed OCR 72B i1 for coding?

For coding workloads, BaichuanMed OCR 72B i1 on NVIDIA A16 64GB receives a C grade with 10.7 tok/s and 24K context.

What context window can BaichuanMed OCR 72B i1 use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, BaichuanMed OCR 72B i1 can safely use up to 24K tokens of context. 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 A16 64GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for NVIDIA A16 64GBSee all hardware for BaichuanMed OCR 72B i1
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