Raises estimated decode speed by about 75%.
~$249 MSRP
![]() |
VOOZH | about |
SmolLM3 3B needs ~6.4 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~24 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
Runs well
Decode
24.0 tok/s
TTFT
8078 ms
Safe context
58K
Memory
6.4 GB / 11.5 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 24.0 tok/s | 4406 ms | 58K |
| Coding | B | Runs well | 24.0 tok/s | 8078 ms | 58K |
| Agentic Coding | B | Runs well | 24.0 tok/s | 11749 ms | 58K |
| Reasoning | B | Runs well | 24.0 tok/s | 9546 ms | 58K |
| RAG | B | Runs well | 24.0 tok/s | 14687 ms | 58K |
How SmolLM3 3B (3B params) fits at each quantization level on MacBook Air M1 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | B55 |
Q3_K_S | 3 | 1.5 GB | Low | B56 |
NVFP4 | 4 | 1.7 GB | Medium | B56 |
Q4_K_M | 4 | 1.8 GB | Medium | B56 |
Q5_K_M | 5 | 2.2 GB | High | B56 |
Q6_K | 6 | 2.5 GB | High | B57 |
Q8_0 | 8 | 3.2 GB | Very High | B58 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | B60 |
Copy-paste commands to run SmolLM3 3B on your machine.
Run
lms load SmolLM3-3B && lms server startUpgrade options
Raises estimated decode speed by about 75%.
~$249 MSRP
Raises estimated decode speed by about 75%.
~$1,999 MSRP