BaichuanMed OCR 72B i1 needs ~57.1 GB but MacBook Pro M4 Max 36GB only has 25.9 GB. Try a smaller quantization or lighter model.
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
31.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.9 tok/s
TTFT
39457 ms
Safe context
4K
Memory
57.1 GB / 25.9 GB
Offload
50%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 57.1 GB, but this setup only exposes 25.9 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 4.9 tok/s | 21522 ms | 4K |
| Coding | F | Too heavy | 4.9 tok/s | 39457 ms | 4K |
| Agentic Coding | F | Too heavy | 4.9 tok/s | 57392 ms | 4K |
| Reasoning | F | Too heavy | 4.9 tok/s | 46631 ms | 4K |
| RAG | F | Too heavy | 4.9 tok/s | 71739 ms | 4K |
How BaichuanMed OCR 72B i1 (72B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | F0 |
Q3_K_S | 3 | 35.3 GB | Low | F0 |
NVFP4 | 4 | 40.3 GB | Medium | F0 |
Q4_K_M | 4 | 43.9 GB | Medium | F0 |
Q5_K_M | 5 | 51.8 GB | High | F0 |
Q6_K | 6 | 59.0 GB | High | F0 |
Q8_0 | 8 | 77.0 GB | Very High | F0 |
F16 | 16 | 147.6 GB | Maximum | F0 |
Opciones de mejora
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$2,499 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$2,499 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$3,199 MSRP
NVIDIA H100 80GBMayor saltoHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$40,000 MSRP
No, BaichuanMed OCR 72B i1 requires more memory than MacBook Pro M4 Max 36GB provides.
BaichuanMed OCR 72B i1 (72B parameters) requires approximately 57.1 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 M4 Max 36GB, BaichuanMed OCR 72B i1 achieves approximately 4.9 tokens per second decode speed with a time-to-first-token of 39457ms using Q4_K_M quantization.
For coding workloads, BaichuanMed OCR 72B i1 on MacBook Pro M4 Max 36GB receives a F grade with 4.9 tok/s and 4K context.
On MacBook Pro M4 Max 36GB, BaichuanMed OCR 72B i1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. MacBook Pro M4 Max 36GB 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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