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.
Operating mode
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
Fit status
Tight fit
Decode
10.7 tok/s
TTFT
18169 ms
Safe context
24K
Memory
60.0 GB / 64.0 GB
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| 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 |
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 |
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
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickRaises estimated decode speed by about 221%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueRaises estimated decode speed by about 185%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
NVIDIA H20 96GBNVIDIA upgradeRaises estimated decode speed by about 590%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP
Yes, NVIDIA A16 64GB can run BaichuanMed OCR 72B i1 with a C grade (Tight fit). Expected decode speed: 10.7 tok/s.
BaichuanMed OCR 72B i1 (72B parameters) requires approximately 60.0 GB of memory with Q4_K_M quantization.
The recommended quantization for BaichuanMed OCR 72B i1 is Q4_K_M, which balances quality and memory efficiency.
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.
For coding workloads, BaichuanMed OCR 72B i1 on NVIDIA A16 64GB receives a C grade with 10.7 tok/s and 24K context.
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.
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-a16-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 24K |
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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.