RTX PRO 6000 Blackwell Workstation Edition 96GBOpção econômica1792 GB/s (+1246)
C
Raises estimated decode speed by about 150%.34.3 tok/s decodificação
Raises estimated decode speed by about 150%.
~$9,999 MSRP
Can BaichuanMed OCR 72B i1 run on MacBook Pro M4 Max 128GB?
YES — Runs Great
C50Usable
Estimated from fit model
BaichuanMed OCR 72B i1 needs ~67.1 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~14 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 67.1 GB, 13.7 tok/s, Runs well
67.1 GB required92.2 GB available
73% VRAM used
Fit status
Runs well
Decode
13.7 tok/s
TTFT
14158 ms
Safe context
64K
Memory
67.1 GB / 92.2 GB
Memory breakdown
Weights43.9 GB
KV Cache8.4 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsBaichuanMed OCR 72B i1 on MacBook Pro M4 Max 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: 13.7 tok/s decode · 14.2s TTFT (warm) · 34 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 13.7 tok/s | 7722 ms | 64K |
| Coding | C | Runs well | 13.7 tok/s | 14158 ms | 64K |
| Agentic Coding | C | Runs well | 13.7 tok/s | 20593 ms | 64K |
| Reasoning | C | Runs well | 13.7 tok/s | 16732 ms | 64K |
| RAG | C | Runs well | 13.7 tok/s | 25741 ms | 64K |
Quantization options
How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | C42 |
Q3_K_S | 3 | 35.3 GB | Low | C44 |
NVFP4 | 4 | 40.3 GB | Medium | C45 |
Q4_K_M | 4 | 43.9 GB | Medium | C46 |
Q5_K_M | 5 | 51.8 GB | High | C47 |
Q6_K | 6 | 59.0 GB | High | C47 |
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 startOpções de upgrade
Hardware que roda bem BaichuanMed OCR 72B i1
Frequently asked questions
Can MacBook Pro M4 Max 128GB run BaichuanMed OCR 72B i1?
Yes, MacBook Pro M4 Max 128GB can run BaichuanMed OCR 72B i1 with a C grade (Runs well). Expected decode speed: 13.7 tok/s.
How much VRAM does BaichuanMed OCR 72B i1 need?
BaichuanMed OCR 72B i1 (72B parameters) requires approximately 67.1 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 MacBook Pro M4 Max 128GB?
On MacBook Pro M4 Max 128GB, BaichuanMed OCR 72B i1 achieves approximately 13.7 tokens per second decode speed with a time-to-first-token of 14158ms using Q4_K_M quantization.
Can MacBook Pro M4 Max 128GB run BaichuanMed OCR 72B i1 for coding?
For coding workloads, BaichuanMed OCR 72B i1 on MacBook Pro M4 Max 128GB receives a C grade with 13.7 tok/s and 64K context.
What context window can BaichuanMed OCR 72B i1 use on MacBook Pro M4 Max 128GB?
On MacBook Pro M4 Max 128GB, BaichuanMed OCR 72B i1 can safely use up to 64K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 Max 128GB as fast as VRAM for BaichuanMed OCR 72B i1?
Not always. MacBook Pro M4 Max 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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