96 GB VRAM (+32)1792 GB/s (+1192)
C
Raises estimated decode speed by about 221%.34.3 tok/s decode
Raises estimated decode speed by about 221%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
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
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.
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
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 10.7 tok/s | 9910 ms | 24K |
| Coding | C | Tight fit | 10.7 tok/s | 18169 ms | 24K |
| Agentic Coding | D | Runs with offload (needs ~2.8 GB host RAM) | 6.9 tok/s | 40523 ms | 24K |
| Reasoning | C | Tight fit | 10.7 tok/s | 21472 ms | 24K |
| RAG | D | Runs with offload (needs ~2.8 GB host RAM) | 6.9 tok/s | 50654 ms | 24K |
Quantization options
How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | C46 |
Q3_K_S | 3 | 35.3 GB | Low | C47 |
NVFP4 | 4 | 40.3 GB | Medium | C47 |
Q4_K_M | 4 | 43.9 GB | Medium | C47 |
Q5_K_MBest for your GPU | 5 | 51.8 GB | High | C47 |
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 startUpgrade options
Hardware that runs BaichuanMed OCR 72B i1 well
96 GB VRAM (+32)1597 GB/s (+997)
C
Raises estimated decode speed by about 185%.30.5 tok/s decode
Raises estimated decode speed by about 185%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
96 GB VRAM (+32)4000 GB/s (+3400)
C
Raises estimated decode speed by about 590%.73.8 tok/s decode
Raises estimated decode speed by about 590%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP