NVIDIA H200 141GB经济之选141 GB VRAM (+13)4800 GB/s (+1600)
C
Raises estimated decode speed by about 62%.91.8 tok/s 解码
Raises estimated decode speed by about 62%.
~$30,000 MSRP
Can BaichuanMed OCR 72B i1 run on AMD Instinct MI250X 128GB?
YES — Runs Great
C51Usable
Estimated from fit model
BaichuanMed OCR 72B i1 needs ~66.1 GB VRAM. AMD Instinct MI250X 128GB has 128.0 GB. With Q4_K_M quantization, expect ~57 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 66.1 GB, 56.8 tok/s, Runs well
66.1 GB required128.0 GB available
52% VRAM used
Fit status
Runs well
Decode
56.8 tok/s
TTFT
3407 ms
Safe context
133K
Memory
66.1 GB / 128.0 GB
Memory breakdown
Weights43.9 GB
KV Cache8.4 GB
Runtime0.9 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsBaichuanMed OCR 72B i1 on AMD Instinct MI250X 128GB
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: 56.8 tok/s decode · 3.4s TTFT (warm) · 142 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 | 56.8 tok/s | 1858 ms | 133K |
| Coding | C | Runs well | 56.8 tok/s | 3407 ms | 133K |
| Agentic Coding | C | Runs well | 56.8 tok/s | 4955 ms | 133K |
| Reasoning | C | Runs well | 56.8 tok/s | 4026 ms | 133K |
| RAG | C | Runs well | 56.8 tok/s | 6194 ms | 133K |
Quantization options
How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on AMD Instinct MI250X 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | D40 |
Q3_K_S | 3 | 35.3 GB | Low | C41 |
NVFP4 | 4 | 40.3 GB | Medium | C42 |
Q4_K_M | 4 | 43.9 GB | Medium | C43 |
Q5_K_M | 5 | 51.8 GB | High | C44 |
Q6_K | 6 | 59.0 GB | High | C45 |
Q8_0Best for your GPU | 8 | 77.0 GB | Very High | C47 |
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 start升级选项
能流畅运行 BaichuanMed OCR 72B i1 的硬件
Frequently asked questions
Can AMD Instinct MI250X 128GB run BaichuanMed OCR 72B i1?
Yes, AMD Instinct MI250X 128GB can run BaichuanMed OCR 72B i1 with a C grade (Runs well). Expected decode speed: 56.8 tok/s.
How much VRAM does BaichuanMed OCR 72B i1 need?
BaichuanMed OCR 72B i1 (72B parameters) requires approximately 66.1 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 AMD Instinct MI250X 128GB?
On AMD Instinct MI250X 128GB, BaichuanMed OCR 72B i1 achieves approximately 56.8 tokens per second decode speed with a time-to-first-token of 3407ms using Q4_K_M quantization.
Can AMD Instinct MI250X 128GB run BaichuanMed OCR 72B i1 for coding?
For coding workloads, BaichuanMed OCR 72B i1 on AMD Instinct MI250X 128GB receives a C grade with 56.8 tok/s and 133K context.
What context window can BaichuanMed OCR 72B i1 use on AMD Instinct MI250X 128GB?
On AMD Instinct MI250X 128GB, BaichuanMed OCR 72B i1 can safely use up to 133K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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