Vicuna 13B needs ~24.9 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~14 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 with offload
Decode
13.8 tok/s
TTFT
14021 ms
Safe context
4K
Memory
24.9 GB / 25.9 GB
This setup is broadly balanced for this model.
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.
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 | A | Runs well | 13.8 tok/s | 7648 ms | 4K |
| Coding | B | Runs with offload | 13.8 tok/s | 14021 ms | 4K |
| Agentic Coding | F | Too heavy | 8.5 tok/s | 33088 ms | 4K |
| Reasoning | B | Runs with offload | 13.8 tok/s | 16570 ms | 4K |
| RAG | F | Too heavy | 8.5 tok/s | 41360 ms | 4K |
How Vicuna 13B (13B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B66 |
Q3_K_S | 3 | 6.4 GB | Low | B66 |
NVFP4 | 4 | 7.3 GB | Medium | B67 |
Q4_K_M | 4 | 7.9 GB | Medium | B67 |
Q5_K_M | 5 | 9.4 GB | High | B68 |
Q6_K | 6 | 10.7 GB | High | B69 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | A71 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Copy-paste commands to run Vicuna 13B on your machine.
Run
ollama run vicuna:13b升级选项
Raises estimated decode speed by about 69%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Raises estimated decode speed by about 177%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 120%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M3 Pro 36GB can run Vicuna 13B with a B grade (Runs with offload). Expected decode speed: 13.8 tok/s.
Vicuna 13B (13B parameters) requires approximately 24.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Vicuna 13B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, Vicuna 13B achieves approximately 13.8 tokens per second decode speed with a time-to-first-token of 14021ms using Q4_K_M quantization.
For coding workloads, Vicuna 13B on MacBook Pro M3 Pro 36GB receives a B grade with 13.8 tok/s and 4K context.
On MacBook Pro M3 Pro 36GB, Vicuna 13B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M3 Pro 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/vicuna-13b-on-m3-pro-36gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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