HelpingAI 3B hindi needs ~16.9 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~42 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
42.0 tok/s
TTFT
4610 ms
Safe context
3.4M
Memory
16.9 GB / 92.2 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 | 42.0 tok/s | 2514 ms | 3.4M |
| Coding | C | Runs well | 42.0 tok/s | 4610 ms | 3.4M |
| Agentic Coding | C | Runs well | 42.0 tok/s | 6705 ms | 3.4M |
| Reasoning | C | Runs well | 42.0 tok/s | 5448 ms | 3.4M |
| RAG | C | Runs well | 42.0 tok/s | 8381 ms | 3.4M |
How HelpingAI 3B hindi (3B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | D39 |
Q3_K_S | 3 | 1.5 GB | Low | D39 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI 3B hindi on your machine.
Run
lms load hf-mradermacher--helpingai-3b-hindi-gguf && lms server startYes, Mac Studio M2 Ultra 128GB can run HelpingAI 3B hindi with a C grade (Runs well). Expected decode speed: 42.0 tok/s.
HelpingAI 3B hindi (3B parameters) requires approximately 16.9 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 Mac Studio M2 Ultra 128GB, HelpingAI 3B hindi achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
For coding workloads, HelpingAI 3B hindi on Mac Studio M2 Ultra 128GB receives a C grade with 42.0 tok/s and 3.4M context.
On Mac Studio M2 Ultra 128GB, HelpingAI 3B hindi can safely use up to 3.4M 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-ultra-128gb" 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 |
| D39 |
Q4_K_M | 4 | 1.8 GB | Medium | D39 |
Q5_K_M | 5 | 2.2 GB | High | D39 |
Q6_K | 6 | 2.5 GB | High | D39 |
Q8_0 | 8 | 3.2 GB | Very High | D39 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | D39 |
Not always. Mac Studio M2 Ultra 128GB 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.