HelpingAI 3B hindi i1 needs ~5.7 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~37 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
37.2 tok/s
TTFT
5210 ms
Safe context
544K
Memory
5.7 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 | 37.2 tok/s | 2842 ms | 544K |
| Coding | C | Runs well | 37.2 tok/s | 5210 ms | 544K |
| Agentic Coding | C | Runs well | 37.2 tok/s | 7578 ms | 544K |
| Reasoning | C | Runs well | 37.2 tok/s | 6157 ms | 544K |
| RAG | C | Runs well | 37.2 tok/s | 9473 ms | 544K |
How HelpingAI 3B hindi i1 (3B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | C45 |
Q3_K_S | 3 | 1.5 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI 3B hindi i1 on your machine.
Run
lms load hf-mradermacher--helpingai-3b-hindi-i1-gguf && lms server startYes, MacBook Pro M3 24GB can run HelpingAI 3B hindi i1 with a C grade (Runs well). Expected decode speed: 37.2 tok/s.
HelpingAI 3B hindi i1 (3B parameters) requires approximately 5.7 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 3B hindi i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 24GB, HelpingAI 3B hindi i1 achieves approximately 37.2 tokens per second decode speed with a time-to-first-token of 5210ms using Q4_K_M quantization.
For coding workloads, HelpingAI 3B hindi i1 on MacBook Pro M3 24GB receives a C grade with 37.2 tok/s and 544K context.
On MacBook Pro M3 24GB, HelpingAI 3B hindi i1 can safely use up to 544K 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-i1-gguf-on-m3-24gb" 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 |
| C45 |
Q4_K_M | 4 | 1.8 GB | Medium | C45 |
Q5_K_M | 5 | 2.2 GB | High | C45 |
Q6_K | 6 | 2.5 GB | High | C46 |
Q8_0 | 8 | 3.2 GB | Very High | C46 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | C49 |
Not always. MacBook Pro 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.