BaichuanMed OCR 72B i1 needs ~54.8 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With NVFP4 quantization, expect ~12 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
10.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
8.9 tok/s
TTFT
21729 ms
Safe context
4K
Memory
58.4 GB / 48.0 GB
Offload
20%
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~5 GB host RAM) | 10.4 tok/s | 10120 ms | 4K |
| Coding | F | Too heavy | 8.9 tok/s | 21729 ms | 4K |
| Agentic Coding | F | Too heavy | 6.7 tok/s | 41997 ms | 4K |
| Reasoning | F | Too heavy | 8.9 tok/s | 25680 ms | 4K |
| RAG | F | Too heavy | 6.7 tok/s | 52497 ms | 4K |
How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on RTX 6000 Ada 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 |
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
NVIDIA A16 64GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$6,500 MSRP
RTX PRO 6000 Blackwell Workstation Edition 96GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$9,999 MSRP
Yes, RTX 6000 Ada 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: 11.7 tok/s.
BaichuanMed OCR 72B i1 (72B parameters) requires approximately 58.4 GB at Q4_K_M quantization. On RTX 6000 Ada 48GB, it fits at NVFP4 using 54.8 GB.
The recommended quantization is Q4_K_M, but on RTX 6000 Ada 48GB the best fitting quantization is NVFP4, which uses 54.8 GB.
On RTX 6000 Ada 48GB, BaichuanMed OCR 72B i1 achieves approximately 11.7 tokens per second decode speed with a time-to-first-token of 16615ms using NVFP4 quantization.
For coding workloads, BaichuanMed OCR 72B i1 on RTX 6000 Ada 48GB receives a F grade with 8.9 tok/s and 4K context.
On RTX 6000 Ada 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.
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-rtx-6000-ada-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
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.