MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+12)
C
This setup is broadly balanced for this model.70 tok/s decode
~$2,499 MSRP
HelpingAI2.5 5B i1 needs ~6.9 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~70 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
70.0 tok/s
TTFT
2766 ms
Safe context
482K
Memory
6.9 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 | 70.0 tok/s | 1509 ms | 482K |
| Coding | C | Runs well | 70.0 tok/s | 2766 ms | 482K |
| Agentic Coding | C | Runs well | 70.0 tok/s | 4023 ms | 482K |
| Reasoning | C | Runs well | 70.0 tok/s | 3269 ms | 482K |
| RAG | C | Runs well | 70.0 tok/s | 5029 ms | 482K |
How HelpingAI2.5 5B i1 (5B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.0 GB | Low | C44 |
Q3_K_S | 3 | 2.5 GB | Low | C44 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2.5 5B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-5b-i1-gguf && lms server startUpgrade options
Yes, Intel Arc Pro B60 24GB can run HelpingAI2.5 5B i1 with a C grade (Runs well). Expected decode speed: 70.0 tok/s.
HelpingAI2.5 5B i1 (5B parameters) requires approximately 6.9 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2.5 5B i1 is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B60 24GB, HelpingAI2.5 5B i1 achieves approximately 70.0 tokens per second decode speed with a time-to-first-token of 2766ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 5B i1 on Intel Arc Pro B60 24GB receives a C grade with 70.0 tok/s and 482K context.
On Intel Arc Pro B60 24GB, HelpingAI2.5 5B i1 can safely use up to 482K 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--helpingai2-5-5b-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:
2.8 GB |
| Medium |
| C44 |
Q4_K_M | 4 | 3.1 GB | Medium | C44 |
Q5_K_M | 5 | 3.6 GB | High | C44 |
Q6_K | 6 | 4.1 GB | High | C44 |
Q8_0 | 8 | 5.4 GB | Very High | C45 |
F16Best for your GPU | 16 | 10.3 GB | Maximum | C48 |
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.