HelpingAI 15B i1 needs ~14.2 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~27 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
26.9 tok/s
TTFT
7194 ms
Safe context
105K
Memory
14.2 GB / 24.0 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 | 26.9 tok/s | 3924 ms | 105K |
| Coding | C | Runs well | 26.9 tok/s | 7194 ms | 105K |
| Agentic Coding | C | Runs well | 26.9 tok/s | 10464 ms | 105K |
| Reasoning | C | Runs well | 26.9 tok/s | 8502 ms | 105K |
| RAG | C | Runs well | 26.9 tok/s | 13080 ms | 105K |
How HelpingAI 15B i1 (15B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C46 |
Q3_K_S | 3 | 7.4 GB | Low | C46 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI 15B i1 on your machine.
Run
lms load hf-mradermacher--helpingai-15b-i1-gguf && lms server startUpgrade options
RTX 5090 32GBBudget pickRaises estimated decode speed by about 361%.
Adds memory headroom for longer context windows and future model growth.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueRaises estimated decode speed by about 206%.
Adds memory headroom for longer context windows and future model growth.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
~$2,499 MSRP
Yes, Intel Arc Pro B60 24GB can run HelpingAI 15B i1 with a C grade (Runs well). Expected decode speed: 26.9 tok/s.
HelpingAI 15B i1 (15B parameters) requires approximately 14.2 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 15B i1 is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B60 24GB, HelpingAI 15B i1 achieves approximately 26.9 tokens per second decode speed with a time-to-first-token of 7194ms using Q4_K_M quantization.
For coding workloads, HelpingAI 15B i1 on Intel Arc Pro B60 24GB receives a C grade with 26.9 tok/s and 105K context.
On Intel Arc Pro B60 24GB, HelpingAI 15B i1 can safely use up to 105K 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-15b-i1-gguf-on-arc-pro-b60-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
8.4 GB |
| Medium |
| C47 |
Q4_K_M | 4 | 9.2 GB | Medium | C48 |
Q5_K_M | 5 | 10.8 GB | High | C49 |
Q6_K | 6 | 12.3 GB | High | C50 |
Q8_0Best for your GPU | 8 | 16.1 GB | Very High | C49 |
F16 | 16 | 30.7 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.