Mac mini M4 64GBBudget pick
64 GB Unified (+28)
B
Adds memory headroom for longer context windows and future model growth.8.3 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Nous Dolphin 13B needs ~26.7 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q5_K_M quantization, expect ~11 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
0.8 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
11.3 tok/s
TTFT
17197 ms
Safe context
15K
Memory
26.7 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 | 11.9 tok/s | 8850 ms | 15K |
| Coding | B | Runs with offload (needs ~0.3 GB host RAM) | 11.3 tok/s | 17197 ms | 15K |
| Agentic Coding | F | Too heavy | 7.0 tok/s | 40336 ms | 15K |
| Reasoning | B | Runs with offload (needs ~0.3 GB host RAM) | 11.3 tok/s | 20323 ms | 15K |
| RAG | F | Too heavy | 7.0 tok/s | 50420 ms |
How Nous Dolphin 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 |
Copy-paste commands to run Nous Dolphin 13B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "nousresearch/Nous-Dolphin-13B" \
--hf-file "Nous-Dolphin-13B-Q5_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Raises estimated decode speed by about 79%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Raises estimated decode speed by about 192%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M3 Pro 36GB can run Nous Dolphin 13B with a B grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 11.3 tok/s.
Nous Dolphin 13B (13B parameters) requires approximately 26.7 GB of memory with Q5_K_M quantization.
The recommended quantization for Nous Dolphin 13B is Q5_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, Nous Dolphin 13B achieves approximately 11.3 tokens per second decode speed with a time-to-first-token of 17197ms using Q5_K_M quantization.
For coding workloads, Nous Dolphin 13B on MacBook Pro M3 Pro 36GB receives a B grade with 11.3 tok/s and 15K context.
On MacBook Pro M3 Pro 36GB, Nous Dolphin 13B can safely use up to 15K tokens of context. The model's official context limit is 16K, 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/nous-dolphin-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>
Preview:
| 15K |
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 |
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.