Intel Arc Pro B60 24GBBest value456 GB/s (+256)
C
Raises estimated decode speed by about 76%.26.9 tok/s decode
Raises estimated decode speed by about 76%.
~$599 MSRP
HelpingAI 15B i1 needs ~15.3 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~15 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
15.3 tok/s
TTFT
12653 ms
Safe context
87K
Memory
15.3 GB / 23.0 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 15.3 tok/s | 6902 ms | 87K |
| Coding | C | Runs well | 15.3 tok/s | 12653 ms | 87K |
| Agentic Coding | C | Runs well | 15.3 tok/s | 18405 ms | 87K |
| Reasoning | C | Runs well | 15.3 tok/s | 14954 ms | 87K |
| RAG | C | Runs well | 15.3 tok/s | 23006 ms | 87K |
How HelpingAI 15B i1 (15B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C46 |
Q3_K_S | 3 | 7.4 GB | Low | C47 |
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
Intel Arc Pro B60 24GBBest valueRaises estimated decode speed by about 76%.
~$599 MSRP
Raises estimated decode speed by about 93%.
~$2,499 MSRP
Yes, MacBook Pro M2 Pro 32GB can run HelpingAI 15B i1 with a C grade (Runs well). Expected decode speed: 15.3 tok/s.
HelpingAI 15B i1 (15B parameters) requires approximately 15.3 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 MacBook Pro M2 Pro 32GB, HelpingAI 15B i1 achieves approximately 15.3 tokens per second decode speed with a time-to-first-token of 12653ms using Q4_K_M quantization.
For coding workloads, HelpingAI 15B i1 on MacBook Pro M2 Pro 32GB receives a C grade with 15.3 tok/s and 87K context.
On MacBook Pro M2 Pro 32GB, HelpingAI 15B i1 can safely use up to 87K 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-m2-pro-32gb" 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 |
| C48 |
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 |
Not always. MacBook Pro M2 Pro 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.