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%.19.9 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 169%.
~$1,999 MSRP
Can Yi 1.5 9B Chat run on MacBook Air M1 16GB?
YES — Runs Great
C49Usable
Estimated from fit model
Yi 1.5 9B Chat needs ~9.2 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~7 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) — 9.2 GB, 7.4 tok/s, Runs well
9.2 GB required11.5 GB available
80% VRAM used
Fit status
Runs well
Decode
7.4 tok/s
TTFT
26051 ms
Safe context
52K
Memory
9.2 GB / 11.5 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom1.7 GB
See how fast it feels
See how fast it feelsYi 1.5 9B Chat 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: 7.4 tok/s decode · 26.1s TTFT (warm) · 19 tok/s prefill
What limits this setup
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 7.4 tok/s | 14209 ms | 52K |
| Coding | C | Runs well | 7.4 tok/s | 26051 ms | 52K |
| Agentic Coding | C | Tight fit | 7.4 tok/s | 37892 ms | 52K |
| Reasoning | C | Runs well | 7.4 tok/s | 30787 ms | 52K |
| RAG | C | Tight fit | 7.4 tok/s | 47365 ms | 52K |
Quantization options
How Yi 1.5 9B Chat (9B params) fits at each quantization level on MacBook Air M1 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C50 |
Q3_K_S | 3 | 4.4 GB | Low | C52 |
NVFP4 | 4 | 5.0 GB | Medium | C52 |
Q4_K_M | 4 | 5.5 GB | Medium | C52 |
Q5_K_M | 5 | 6.5 GB | High | C52 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | C52 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Yi 1.5 9B Chat on your machine.
Run
lms load hf-bartowski--yi-1-5-9b-chat-gguf && lms server startOpciones de mejora
Hardware que ejecuta bien Yi 1.5 9B Chat
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 376%.35.2 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 376%.
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 472%.42.3 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 472%.
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 9B Chat?
Yes, MacBook Air M1 16GB can run Yi 1.5 9B Chat with a C grade (Runs well). Expected decode speed: 7.4 tok/s.
How much VRAM does Yi 1.5 9B Chat need?
Yi 1.5 9B Chat (9B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 1.5 9B Chat?
The recommended quantization for Yi 1.5 9B Chat is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 1.5 9B Chat run at on MacBook Air M1 16GB?
On MacBook Air M1 16GB, Yi 1.5 9B Chat achieves approximately 7.4 tokens per second decode speed with a time-to-first-token of 26051ms using Q4_K_M quantization.
Can MacBook Air M1 16GB run Yi 1.5 9B Chat for coding?
For coding workloads, Yi 1.5 9B Chat on MacBook Air M1 16GB receives a C grade with 7.4 tok/s and 52K context.
What context window can Yi 1.5 9B Chat use on MacBook Air M1 16GB?
On MacBook Air M1 16GB, Yi 1.5 9B Chat can safely use up to 52K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Yi 1.5 9B Chat feels slow on MacBook Air M1 16GB?
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Is unified memory on MacBook Air M1 16GB as fast as VRAM for Yi 1.5 9B Chat?
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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