TinyLlama 1.1B Chat v0.6 needs ~4.3 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~15 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
15.4 tok/s
TTFT
12571 ms
Safe context
1.6M
Memory
4.3 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 | 15.4 tok/s | 6857 ms | 1.0M |
| Coding | C | Runs well | 15.4 tok/s | 12571 ms | 1.6M |
| Agentic Coding | C | Runs well | 15.4 tok/s | 18286 ms | 1.6M |
| Reasoning | C | Runs well | 15.4 tok/s | 14857 ms | 1.6M |
| RAG | C | Runs well | 15.4 tok/s | 22857 ms | 1.6M |
How TinyLlama 1.1B Chat v0.6 (1.100000023841858B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.4 GB | Low | C45 |
Q3_K_S | 3 | 0.5 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run TinyLlama 1.1B Chat v0.6 on your machine.
Run
lms load hf-tinyllama--tinyllama-1-1b-chat-v0-6 && lms server startYes, MacBook Air M3 24GB can run TinyLlama 1.1B Chat v0.6 with a C grade (Runs well). Expected decode speed: 15.4 tok/s.
TinyLlama 1.1B Chat v0.6 (1.100000023841858B parameters) requires approximately 4.3 GB of memory with Q4_K_M quantization.
The recommended quantization for TinyLlama 1.1B Chat v0.6 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M3 24GB, TinyLlama 1.1B Chat v0.6 achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12571ms using Q4_K_M quantization.
For coding workloads, TinyLlama 1.1B Chat v0.6 on MacBook Air M3 24GB receives a C grade with 15.4 tok/s and 1.6M context.
On MacBook Air M3 24GB, TinyLlama 1.1B Chat v0.6 can safely use up to 1.6M tokens of context. The model's official context limit is —, 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/hf-tinyllama--tinyllama-1-1b-chat-v0-6-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:
0.6 GB |
| Medium |
| C45 |
Q4_K_M | 4 | 0.7 GB | Medium | C45 |
Q5_K_M | 5 | 0.8 GB | High | C45 |
Q6_K | 6 | 0.9 GB | High | C45 |
Q8_0 | 8 | 1.2 GB | Very High | C45 |
F16Best for your GPU | 16 | 2.3 GB | Maximum | C46 |
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.