NVIDIA H20 96GBGünstige Wahl96 GB VRAM (+16)4000 GB/s (+1961)
C
Raises estimated decode speed by about 89%.73.8 tok/s Dekodierung
Raises estimated decode speed by about 89%.
ca. $12,000 MSRP
Can BaichuanMed OCR 72B i1 run on NVIDIA A100 80GB?
YES — Runs Great
C54Usable
Estimated from fit model
BaichuanMed OCR 72B i1 needs ~61.6 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~39 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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) — 61.6 GB, 39.0 tok/s, Runs well
61.6 GB required80.0 GB available
77% VRAM used
Fit status
Runs well
Decode
39.0 tok/s
TTFT
4964 ms
Safe context
51K
Memory
61.6 GB / 80.0 GB
Memory breakdown
Weights43.9 GB
KV Cache8.4 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsBaichuanMed OCR 72B i1 on NVIDIA A100 80GB
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: 39.0 tok/s decode · 5.0s TTFT (warm) · 98 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 39.0 tok/s | 2708 ms | 51K |
| Coding | C | Runs well | 39.0 tok/s | 4964 ms | 51K |
| Agentic Coding | C | Tight fit | 39.0 tok/s | 7221 ms | 51K |
| Reasoning | C | Runs well | 39.0 tok/s | 5867 ms | 51K |
| RAG | C | Tight fit | 39.0 tok/s | 9026 ms | 51K |
Inference speed
BaichuanMed OCR 72B i1 inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for BaichuanMed OCR 72B i1 at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~14 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 13.7 | Fits |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 13.4 | Too big |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 12.7 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 10.6 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 10.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 9.4 | Too big |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 7.1 | Too big |
| RTX 5090 32GB | 32 GB | Q4_K_M | 6.5 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 6.5 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 5.7 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 4.4 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 3.8 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 3.4 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.6 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.4 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.2 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.1 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | C43 |
Q3_K_S | 3 | 35.3 GB | Low | C45 |
NVFP4 | 4 | 40.3 GB | Medium | C47 |
Q4_K_M | 4 | 43.9 GB | Medium | C47 |
Q5_K_M | 5 | 51.8 GB | High | C47 |
Q6_KBest for your GPU | 6 | 59.0 GB | High | C47 |
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-Optionen
Hardware, die BaichuanMed OCR 72B i1 gut ausführt
Frequently asked questions
Can NVIDIA A100 80GB run BaichuanMed OCR 72B i1?
Yes, NVIDIA A100 80GB can run BaichuanMed OCR 72B i1 with a C grade (Runs well). Expected decode speed: 39.0 tok/s.
How much VRAM does BaichuanMed OCR 72B i1 need?
BaichuanMed OCR 72B i1 (72B parameters) requires approximately 61.6 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 A100 80GB?
On NVIDIA A100 80GB, BaichuanMed OCR 72B i1 achieves approximately 39.0 tokens per second decode speed with a time-to-first-token of 4964ms using Q4_K_M quantization.
Can NVIDIA A100 80GB run BaichuanMed OCR 72B i1 for coding?
For coding workloads, BaichuanMed OCR 72B i1 on NVIDIA A100 80GB receives a C grade with 39.0 tok/s and 51K context.
What context window can BaichuanMed OCR 72B i1 use on NVIDIA A100 80GB?
On NVIDIA A100 80GB, BaichuanMed OCR 72B i1 can safely use up to 51K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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