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.
~$1,099 MSRP
Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored needs ~38.4 GB but MacBook Pro M4 Pro 24GB only has 17.3 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
21.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.8 tok/s
TTFT
33292 ms
Safe context
4K
Memory
38.4 GB / 17.3 GB
Offload
50%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 38.4 GB, but this setup only exposes 17.3 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 | 5.8 tok/s | 18159 ms | 4K |
| Coding | F | Too heavy | 5.8 tok/s | 33292 ms | 4K |
| Agentic Coding | F | Too heavy | 5.8 tok/s | 48425 ms | 4K |
| Reasoning | F | Too heavy | 5.8 tok/s | 39345 ms | 4K |
| RAG | F | Too heavy | 5.8 tok/s | 60531 ms | 4K |
How Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored (48B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.7 GB | Low | F0 |
Q3_K_S | 3 | 23.5 GB | Low | F0 |
NVFP4 | 4 | 26.9 GB | Medium | F0 |
Q4_K_M | 4 | 29.3 GB | Medium | F0 |
Q5_K_M | 5 | 34.6 GB | High | F0 |
Q6_K | 6 | 39.4 GB | High | F0 |
Q8_0 | 8 | 51.4 GB | Very High | F0 |
F16 | 16 | 98.4 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.
~$1,099 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.
~$1,599 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
No, Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored requires more memory than MacBook Pro M4 Pro 24GB provides.
Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored (48B parameters) requires approximately 38.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 24GB, Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored achieves approximately 5.8 tokens per second decode speed with a time-to-first-token of 33292ms using Q4_K_M quantization.
For coding workloads, Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored on MacBook Pro M4 Pro 24GB receives a F grade with 5.8 tok/s and 4K context.
On MacBook Pro M4 Pro 24GB, Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored 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 Pro 24GB 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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