RTX 5070 12GBBudget pick672 GB/s (+572)
C
Raises estimated decode speed by about 61%.57 tok/s decode
Raises estimated decode speed by about 61%.
~$549 MSRP
HelpingAI 3B hindi needs ~4.8 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q4_K_M quantization, expect ~36 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
35.5 tok/s
TTFT
5451 ms
Safe context
321K
Memory
4.8 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 | C | Runs well | 35.5 tok/s | 2973 ms | 321K |
| Coding | C | Runs well | 35.5 tok/s | 5451 ms | 321K |
| Agentic Coding | C | Runs well | 35.5 tok/s | 7928 ms | 321K |
| Reasoning | C | Runs well | 35.5 tok/s | 6442 ms | 321K |
| RAG | C | Runs well | 35.5 tok/s | 9910 ms | 321K |
How HelpingAI 3B hindi (3B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | C47 |
Q3_K_S | 3 | 1.5 GB | Low | C47 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI 3B hindi on your machine.
Run
lms load hf-mradermacher--helpingai-3b-hindi-gguf && lms server startUpgrade options
Yes, MacBook Air M2 16GB can run HelpingAI 3B hindi with a C grade (Runs well). Expected decode speed: 35.5 tok/s.
HelpingAI 3B hindi (3B parameters) requires approximately 4.8 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 3B hindi is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M2 16GB, HelpingAI 3B hindi achieves approximately 35.5 tokens per second decode speed with a time-to-first-token of 5451ms using Q4_K_M quantization.
For coding workloads, HelpingAI 3B hindi on MacBook Air M2 16GB receives a C grade with 35.5 tok/s and 321K context.
On MacBook Air M2 16GB, HelpingAI 3B hindi can safely use up to 321K 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-mradermacher--helpingai-3b-hindi-gguf-on-m2-air-16gb" 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 |
| C48 |
Q4_K_M | 4 | 1.8 GB | Medium | C48 |
Q5_K_M | 5 | 2.2 GB | High | C48 |
Q6_K | 6 | 2.5 GB | High | C49 |
Q8_0 | 8 | 3.2 GB | Very High | C49 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | C52 |
Not always. MacBook Air M2 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.