MacBook Pro M3 Pro 36GBOpción económica
36 GB Unified (+4)150 GB/s (+30)
C
Sube la velocidad estimada de decodificación alrededor de un 38%.24.1 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 38%.
~$1,999 MSRP
Can Dolphin 2.9 8B run on Mac mini M4 32GB?
YES — Runs Great
C48Usable
Estimated — low-sample bucket· few comparable runs
Dolphin 2.9 8B needs ~11.2 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~18 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: 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
Q4_K_M (Medium quality) — 11.2 GB, 17.5 tok/s, Runs well
11.2 GB required23.0 GB available
49% VRAM used
Fit status
Runs well
Decode
17.5 tok/s
TTFT
11056 ms
Safe context
33K
Memory
11.2 GB / 23.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsDolphin 2.9 8B on Mac mini M4 32GB
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: 17.5 tok/s decode · 11.1s TTFT (warm) · 44 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 17.5 tok/s | 6031 ms | 33K |
| Coding | C | Runs well | 17.7 tok/s | 10935 ms | 33K |
| Agentic Coding | C | Runs well | 17.5 tok/s | 16082 ms | 33K |
| Reasoning | C | Runs well | 17.5 tok/s | 13067 ms | 33K |
| RAG | C | Runs well | 17.5 tok/s | 20103 ms | 33K |
Quantization options
How Dolphin 2.9 8B (8B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C45 |
Q3_K_S | 3 | 3.9 GB | Low | C46 |
NVFP4 | 4 | 4.5 GB | Medium | C46 |
Q4_K_M | 4 | 4.9 GB | Medium | C46 |
Q5_K_M | 5 | 5.8 GB | High | C47 |
Q6_K | 6 | 6.6 GB | High | C47 |
Q8_0 | 8 | 8.6 GB | Very High | C49 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C50 |
Get started
Copy-paste commands to run Dolphin 2.9 8B on your machine.
Run
ollama run dolphin-llama3Opciones de mejora
Hardware que ejecuta bien Dolphin 2.9 8B
MacBook Pro M4 Max 36GBMejor relación calidad-precio
36 GB Unified (+4)410 GB/s (+290)
C
Sube la velocidad estimada de decodificación alrededor de un 254%.62 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 254%.
~$2,499 MSRP
Mac Studio M2 Ultra 64GBMejora Apple
64 GB Unified (+32)800 GB/s (+680)
C
Sube la velocidad estimada de decodificación alrededor de un 484%.102.2 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 484%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$3,999 MSRP
Frequently asked questions
Can Mac mini M4 32GB run Dolphin 2.9 8B?
Yes, Mac mini M4 32GB can run Dolphin 2.9 8B with a C grade (Runs well). Expected decode speed: 17.7 tok/s.
How much VRAM does Dolphin 2.9 8B need?
Dolphin 2.9 8B (8B parameters) requires approximately 11.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Dolphin 2.9 8B?
The recommended quantization for Dolphin 2.9 8B is Q4_K_M, which balances quality and memory efficiency.
What speed will Dolphin 2.9 8B run at on Mac mini M4 32GB?
On Mac mini M4 32GB, Dolphin 2.9 8B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10935ms using Q4_K_M quantization.
Can Mac mini M4 32GB run Dolphin 2.9 8B for coding?
For coding workloads, Dolphin 2.9 8B on Mac mini M4 32GB receives a C grade with 17.7 tok/s and 33K context.
What context window can Dolphin 2.9 8B use on Mac mini M4 32GB?
On Mac mini M4 32GB, Dolphin 2.9 8B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Is unified memory on Mac mini M4 32GB as fast as VRAM for Dolphin 2.9 8B?
Not always. Mac mini M4 32GB 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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