MacBook Pro M3 Pro 36GBBudget pick
36 GB Unified (+12)
C
This setup is broadly balanced for this model.42 tok/s decode
~$1,999 MSRP
HelpingAI 3B hindi i1 needs ~5.5 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~48 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
48.0 tok/s
TTFT
4033 ms
Safe context
859K
Memory
5.5 GB / 24.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 48.0 tok/s | 2200 ms | 859K |
| Coding | C | Runs well | 48.0 tok/s | 4033 ms | 859K |
| Agentic Coding | C | Runs well | 48.0 tok/s | 5867 ms | 859K |
| Reasoning | C | Runs well | 48.0 tok/s | 4767 ms | 859K |
| RAG | C | Runs well | 48.0 tok/s | 7333 ms | 859K |
How HelpingAI 3B hindi i1 (3B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | C43 |
Q3_K_S | 3 | 1.5 GB | Low | C43 |
NVFP4 | 4 | 1.7 GB | Medium | C43 |
Q4_K_M | 4 | 1.8 GB | Medium | C43 |
Q5_K_M | 5 | 2.2 GB | High | C44 |
Q6_K | 6 | 2.5 GB | High | C44 |
Q8_0 | 8 | 3.2 GB | Very High | C44 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | C46 |
Copy-paste commands to run HelpingAI 3B hindi i1 on your machine.
Run
lms load hf-mradermacher--helpingai-3b-hindi-i1-gguf && lms server startUpgrade options
Yes, NVIDIA L4 24GB can run HelpingAI 3B hindi i1 with a C grade (Runs well). Expected decode speed: 48.0 tok/s.
HelpingAI 3B hindi i1 (3B parameters) requires approximately 5.5 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 NVIDIA L4 24GB, HelpingAI 3B hindi i1 achieves approximately 48.0 tokens per second decode speed with a time-to-first-token of 4033ms using Q4_K_M quantization.
For coding workloads, HelpingAI 3B hindi i1 on NVIDIA L4 24GB receives a C grade with 48.0 tok/s and 859K context.
On NVIDIA L4 24GB, HelpingAI 3B hindi i1 can safely use up to 859K 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-l4-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: