HelpingAI 3B hindi needs ~3.9 GB VRAM. Intel Arc A750 8GB has 8.0 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
203K
Memory
3.9 GB / 8.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 42.0 tok/s | 2514 ms | 203K |
| Coding | C | Runs well | 42.0 tok/s | 4610 ms | 203K |
| Agentic Coding | C | Runs well | 42.0 tok/s | 6705 ms | 203K |
| Reasoning | C | Runs well | 42.0 tok/s | 5448 ms | 203K |
| RAG | C | Runs well | 42.0 tok/s | 8381 ms | 203K |
How HelpingAI 3B hindi (3B params) fits at each quantization level on Intel Arc A750 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | C50 |
Q3_K_S | 3 | 1.5 GB | Low | C50 |
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, Intel Arc A750 8GB 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 3.9 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 Intel Arc A750 8GB, 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 Intel Arc A750 8GB receives a C grade with 42.0 tok/s and 203K context.
On Intel Arc A750 8GB, HelpingAI 3B hindi can safely use up to 203K 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-arc-a750-8gb" 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 |
| C51 |
Q4_K_M | 4 | 1.8 GB | Medium | C51 |
Q5_K_M | 5 | 2.2 GB | High | C52 |
Q6_K | 6 | 2.5 GB | High | C52 |
Q8_0Best for your GPU | 8 | 3.2 GB | Very High | C53 |
F16 | 16 | 6.1 GB | Maximum | F0 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.