SmolLM3 3B needs ~7.3 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~40 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
39.9 tok/s
TTFT
4847 ms
Safe context
98K
Memory
7.3 GB / 17.3 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 | C | Runs well | 39.9 tok/s | 2644 ms | 98K |
| Coding | B | Runs well | 39.9 tok/s | 4847 ms | 98K |
| Agentic Coding | B | Runs well | 39.9 tok/s | 7050 ms | 98K |
| Reasoning | B | Runs well | 39.9 tok/s | 5728 ms | 98K |
| RAG | B | Runs well | 39.9 tok/s | 8812 ms | 98K |
How SmolLM3 3B (3B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | C53 |
Q3_K_S | 3 | 1.5 GB | Low | C53 |
NVFP4 | 4 |
Copy-paste commands to run SmolLM3 3B on your machine.
Run
lms load SmolLM3-3B && lms server startYes, MacBook Air M3 24GB can run SmolLM3 3B with a B grade (Runs well). Expected decode speed: 39.9 tok/s.
SmolLM3 3B (3B parameters) requires approximately 7.3 GB of memory with Q4_K_M quantization.
The recommended quantization for SmolLM3 3B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M3 24GB, SmolLM3 3B achieves approximately 39.9 tokens per second decode speed with a time-to-first-token of 4847ms using Q4_K_M quantization.
For coding workloads, SmolLM3 3B on MacBook Air M3 24GB receives a B grade with 39.9 tok/s and 98K context.
On MacBook Air M3 24GB, SmolLM3 3B can safely use up to 98K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/smollm3-3b-on-m3-air-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
1.7 GB |
| Medium |
| C53 |
Q4_K_M | 4 | 1.8 GB | Medium | C53 |
Q5_K_M | 5 | 2.2 GB | High | C54 |
Q6_K | 6 | 2.5 GB | High | C54 |
Q8_0 | 8 | 3.2 GB | Very High | C54 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | B57 |
Not always. MacBook Air M3 24GB 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.