Yi 1.5 9B needs ~9.6 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~7 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
Fit status
Tight fit
Decode
8.1 tok/s
TTFT
23955 ms
Safe context
4K
Memory
9.6 GB / 11.5 GB
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 8.1 tok/s | 13066 ms | 4K |
| Coding | C | Tight fit | 7.4 tok/s | 26051 ms | 4K |
| Agentic Coding | C | Runs with offload | 8.1 tok/s | 34843 ms | 4K |
| Reasoning | C | Tight fit | 8.1 tok/s | 28310 ms | 4K |
| RAG | C | Runs with offload | 8.1 tok/s | 43554 ms | 4K |
How Yi 1.5 9B (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 | C55 |
Q3_K_S | 3 | 4.4 GB | Low | B56 |
NVFP4 | 4 |
Copy-paste commands to run Yi 1.5 9B on your machine.
Run
lms load Yi-1.5-9B-Chat && lms server startUpgrade options
Raises estimated decode speed by about 94%.
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Raises estimated decode speed by about 94%.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Raises estimated decode speed by about 67%.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
RTX 3080 Ti 12GBBiggest leapRaises estimated decode speed by about 1233%.
~$1,199 MSRP
Yes, MacBook Air M1 16GB can run Yi 1.5 9B with a C grade (Tight fit). Expected decode speed: 7.4 tok/s.
Yi 1.5 9B (9B parameters) requires approximately 9.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 9B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M1 16GB, Yi 1.5 9B achieves approximately 7.4 tokens per second decode speed with a time-to-first-token of 26051ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 9B on MacBook Air M1 16GB receives a C grade with 7.4 tok/s and 4K context.
On MacBook Air M1 16GB, Yi 1.5 9B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/yi-1.5-9b-on-m1-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
5.0 GB |
| Medium |
| B57 |
Q4_K_M | 4 | 5.5 GB | Medium | B57 |
Q5_K_M | 5 | 6.5 GB | High | B57 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | B56 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
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.