BaichuanMed OCR 72B i1 needs ~67.1 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~6 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
Runs well
Decode
5.5 tok/s
TTFT
35429 ms
Safe context
64K
Memory
67.1 GB / 92.2 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 5.5 tok/s | 19325 ms | 64K |
| Coding | C | Runs well | 5.5 tok/s | 35429 ms | 64K |
| Agentic Coding | C | Runs well | 5.5 tok/s | 51533 ms | 64K |
| Reasoning | C | Runs well | 5.5 tok/s | 41870 ms | 64K |
| RAG | C | Runs well | 5.5 tok/s | 64416 ms | 64K |
How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on MacBook Pro M3 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 |
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
RTX PRO 6000 Blackwell Workstation Edition 96GBOpção econômicaRaises estimated decode speed by about 524%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBMelhor custo-benefícioRaises estimated decode speed by about 455%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$9,999 MSRP
Yes, MacBook Pro M3 Max 128GB can run BaichuanMed OCR 72B i1 with a C grade (Runs well). Expected decode speed: 5.5 tok/s.
BaichuanMed OCR 72B i1 (72B parameters) requires approximately 67.1 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 MacBook Pro M3 Max 128GB, BaichuanMed OCR 72B i1 achieves approximately 5.5 tokens per second decode speed with a time-to-first-token of 35429ms using Q4_K_M quantization.
For coding workloads, BaichuanMed OCR 72B i1 on MacBook Pro M3 Max 128GB receives a C grade with 5.5 tok/s and 64K context.
On MacBook Pro M3 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.
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. MacBook Pro M3 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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