HelpingAI2 9B needs ~9.8 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~45 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
44.9 tok/s
TTFT
4316 ms
Safe context
231K
Memory
9.8 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 | 44.9 tok/s | 2354 ms | 231K |
| Coding | C | Runs well | 44.9 tok/s | 4316 ms | 231K |
| Agentic Coding | C | Runs well | 44.9 tok/s | 6278 ms | 231K |
| Reasoning | C | Runs well | 44.9 tok/s | 5101 ms | 231K |
| RAG | C | Runs well | 44.9 tok/s | 7848 ms | 231K |
How HelpingAI2 9B (9B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C44 |
Q3_K_S | 3 | 4.4 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2 9B on your machine.
Run
lms load hf-bartowski--helpingai2-9b-gguf && lms server startUpgrade options
RTX 5090 32GBBudget pickRaises estimated decode speed by about 281%.
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
~$2,499 MSRP
Yes, Intel Arc Pro B60 24GB can run HelpingAI2 9B with a C grade (Runs well). Expected decode speed: 44.9 tok/s.
HelpingAI2 9B (9B parameters) requires approximately 9.8 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2 9B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B60 24GB, HelpingAI2 9B achieves approximately 44.9 tokens per second decode speed with a time-to-first-token of 4316ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 9B on Intel Arc Pro B60 24GB receives a C grade with 44.9 tok/s and 231K context.
On Intel Arc Pro B60 24GB, HelpingAI2 9B can safely use up to 231K 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-bartowski--helpingai2-9b-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:
5.0 GB |
| Medium |
| C45 |
Q4_K_M | 4 | 5.5 GB | Medium | C45 |
Q5_K_M | 5 | 6.5 GB | High | C46 |
Q6_K | 6 | 7.4 GB | High | C46 |
Q8_0 | 8 | 9.6 GB | Very High | C48 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C49 |
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.