MacBook Pro M3 Pro 18GBOpción económica
18 GB Unified (+2)150 GB/s (+82)
C
Sube la velocidad estimada de decodificación alrededor de un 169%.32.5 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 169%.
~$1,999 MSRP
Can Yi 1.5 6B run on MacBook Air M1 16GB?
YES — Runs Great
C49Usable
Estimated from fit model
Yi 1.5 6B needs ~7.3 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~12 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) — 7.3 GB, 12.1 tok/s, Runs well
7.3 GB required11.5 GB available
63% VRAM used
Fit status
Runs well
Decode
12.1 tok/s
TTFT
15970 ms
Safe context
4K
Memory
7.3 GB / 11.5 GB
Memory breakdown
Weights3.7 GB
KV Cache1.0 GB
Runtime0.9 GB
Headroom1.7 GB
See how fast it feels
See how fast it feelsYi 1.5 6B on MacBook Air M1 16GB
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: 12.1 tok/s decode · 16.0s TTFT (warm) · 30 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 | 12.1 tok/s | 8711 ms | 4K |
| Coding | C | Runs well | 12.1 tok/s | 15970 ms | 4K |
| Agentic Coding | C | Runs well | 12.1 tok/s | 23229 ms | 4K |
| Reasoning | C | Runs well | 12.1 tok/s | 18873 ms | 4K |
| RAG | C | Runs well | 12.1 tok/s | 29036 ms | 4K |
Quantization options
How Yi 1.5 6B (6B params) fits at each quantization level on MacBook Air M1 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C49 |
Q3_K_S | 3 | 2.9 GB | Low | C49 |
NVFP4 | 4 | 3.4 GB | Medium | C50 |
Q4_K_M | 4 | 3.7 GB | Medium | C50 |
Q5_K_M | 5 | 4.3 GB | High | C51 |
Q6_K | 6 | 4.9 GB | High | C52 |
Q8_0Best for your GPU | 8 | 6.4 GB | Very High | C52 |
F16 | 16 | 12.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run Yi 1.5 6B on your machine.
Run
lms load Yi-1.5-6B-Chat && lms server startOpciones de mejora
Hardware que ejecuta bien Yi 1.5 6B
MacBook Pro M4 Pro 24GBMejor relación calidad-precio
24 GB Unified (+8)273 GB/s (+205)
C
Sube la velocidad estimada de decodificación alrededor de un 375%.57.5 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 375%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,999 MSRP
MacBook Pro M2 Max 32GBMejora Apple
32 GB Unified (+16)400 GB/s (+332)
C
Sube la velocidad estimada de decodificación alrededor de un 469%.68.9 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 469%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,999 MSRP
Frequently asked questions
Can MacBook Air M1 16GB run Yi 1.5 6B?
Yes, MacBook Air M1 16GB can run Yi 1.5 6B with a C grade (Runs well). Expected decode speed: 12.1 tok/s.
How much VRAM does Yi 1.5 6B need?
Yi 1.5 6B (6B parameters) requires approximately 7.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 1.5 6B?
The recommended quantization for Yi 1.5 6B is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 1.5 6B run at on MacBook Air M1 16GB?
On MacBook Air M1 16GB, Yi 1.5 6B achieves approximately 12.1 tokens per second decode speed with a time-to-first-token of 15970ms using Q4_K_M quantization.
Can MacBook Air M1 16GB run Yi 1.5 6B for coding?
For coding workloads, Yi 1.5 6B on MacBook Air M1 16GB receives a C grade with 12.1 tok/s and 4K context.
What context window can Yi 1.5 6B use on MacBook Air M1 16GB?
On MacBook Air M1 16GB, Yi 1.5 6B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Is unified memory on MacBook Air M1 16GB as fast as VRAM for Yi 1.5 6B?
Not always. MacBook Air M1 16GB 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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