Mac mini M4 64GB经济之选
64 GB Unified (+28)
B
Adds memory headroom for longer context windows and future model growth.8.3 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Can Nous Dolphin 13B run on MacBook Pro M3 Pro 36GB?
YES — With Offload
B68Good
Estimated from fit model
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.
Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
Operating mode
Choose the run profile you care about
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
Q5_K_M (High quality) — 26.7 GB, 11.3 tok/s, Runs with offload (needs ~0.3 GB host RAM)
26.7 GB required25.9 GB available
103% VRAM needed
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
Memory breakdown
Weights9.4 GB
KV Cache12.2 GB
Runtime1.2 GB
Headroom3.9 GB
See how fast it feels
See how fast it feelsNous Dolphin 13B on MacBook Pro M3 Pro 36GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 11.3 tok/s decode · 17.2s TTFT (warm) · 28 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| 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 | 15K |
Quantization options
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 | 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 |
Get started
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 99升级选项
能流畅运行 Nous Dolphin 13B 的硬件
MacBook Pro M4 Pro 64GB最佳性价比
64 GB Unified (+28)273 GB/s (+123)
A
Raises estimated decode speed by about 79%.20.2 tok/s 解码
Raises estimated decode speed by about 79%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
MacBook Pro M4 Max 48GBApple 升级
48 GB Unified (+12)546 GB/s (+396)
A
Raises estimated decode speed by about 192%.33 tok/s 解码
Raises estimated decode speed by about 192%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Frequently asked questions
Can MacBook Pro M3 Pro 36GB run Nous Dolphin 13B?
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.
How much VRAM does Nous Dolphin 13B need?
Nous Dolphin 13B (13B parameters) requires approximately 26.7 GB of memory with Q5_K_M quantization.
What is the best quantization for Nous Dolphin 13B?
The recommended quantization for Nous Dolphin 13B is Q5_K_M, which balances quality and memory efficiency.
What speed will Nous Dolphin 13B run at on MacBook Pro M3 Pro 36GB?
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.
Can MacBook Pro M3 Pro 36GB run Nous Dolphin 13B for coding?
For coding workloads, Nous Dolphin 13B on MacBook Pro M3 Pro 36GB receives a B grade with 11.3 tok/s and 15K context.
What context window can Nous Dolphin 13B use on MacBook Pro M3 Pro 36GB?
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.
What should I upgrade first if Nous Dolphin 13B feels slow on MacBook Pro M3 Pro 36GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Is unified memory on MacBook Pro M3 Pro 36GB as fast as VRAM for Nous Dolphin 13B?
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.
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