MacBook Pro M3 Max 128GBBudget pick
128 GB Unified (+32)
C
Adds memory headroom for longer context windows and future model growth.5.5 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
BaichuanMed OCR 72B i1 needs ~63.6 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~5 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
Tight fit
Decode
5.3 tok/s
TTFT
36650 ms
Safe context
26K
Memory
63.6 GB / 69.1 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.
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.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 5.3 tok/s | 19991 ms | 26K |
| Coding | C | Tight fit | 5.3 tok/s | 36650 ms | 26K |
| Agentic Coding | C | Runs with offload | 4.9 tok/s | 57783 ms | 26K |
| Reasoning | C | Tight fit | 5.3 tok/s | 43314 ms | 26K |
| RAG | C | Runs with offload | 4.9 tok/s | 72229 ms | 26K |
How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | C45 |
Q3_K_S | 3 | 35.3 GB | Low | C47 |
NVFP4 | 4 |
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
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 89%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
NVIDIA H100 80GBBiggest leapRaises estimated decode speed by about 1109%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$40,000 MSRP
Yes, MacBook Pro M2 Max 96GB can run BaichuanMed OCR 72B i1 with a C grade (Tight fit). Expected decode speed: 5.3 tok/s.
BaichuanMed OCR 72B i1 (72B parameters) requires approximately 63.6 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 M2 Max 96GB, BaichuanMed OCR 72B i1 achieves approximately 5.3 tokens per second decode speed with a time-to-first-token of 36650ms using Q4_K_M quantization.
For coding workloads, BaichuanMed OCR 72B i1 on MacBook Pro M2 Max 96GB receives a C grade with 5.3 tok/s and 26K context.
On MacBook Pro M2 Max 96GB, BaichuanMed OCR 72B i1 can safely use up to 26K tokens of context. 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-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
40.3 GB |
| Medium |
| C47 |
Q4_K_M | 4 | 43.9 GB | Medium | C47 |
Q5_K_MBest for your GPU | 5 | 51.8 GB | High | C47 |
Q6_K | 6 | 59.0 GB | High | F0 |
Q8_0 | 8 | 77.0 GB | Very High | F0 |
F16 | 16 | 147.6 GB | Maximum | F0 |
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 M2 Max 96GB 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.