Intel Arc Pro B60 24GBBest value456 GB/s (+256)
C
Raises estimated decode speed by about 76%.39.6 tok/s decode
Raises estimated decode speed by about 76%.
~$599 MSRP
Helply 10.2b chat i1 needs ~11.8 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~23 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
22.5 tok/s
TTFT
8604 ms
Safe context
167K
Memory
11.8 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 | 22.5 tok/s | 4693 ms | 167K |
| Coding | C | Runs well | 22.5 tok/s | 8604 ms | 167K |
| Agentic Coding | C | Runs well | 22.5 tok/s | 12515 ms | 167K |
| Reasoning | C | Runs well | 22.5 tok/s | 10169 ms | 167K |
| RAG | C | Runs well | 22.5 tok/s | 15644 ms | 167K |
How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.0 GB | Low | C45 |
Q3_K_S | 3 | 5.0 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run Helply 10.2b chat i1 on your machine.
Run
lms load hf-mradermacher--helply-10-2b-chat-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 84%.
~$2,499 MSRP
Yes, MacBook Pro M2 Pro 32GB can run Helply 10.2b chat i1 with a C grade (Runs well). Expected decode speed: 22.5 tok/s.
Helply 10.2b chat i1 (10.199999809265137B parameters) requires approximately 11.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Helply 10.2b chat i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, Helply 10.2b chat i1 achieves approximately 22.5 tokens per second decode speed with a time-to-first-token of 8604ms using Q4_K_M quantization.
For coding workloads, Helply 10.2b chat i1 on MacBook Pro M2 Pro 32GB receives a C grade with 22.5 tok/s and 167K context.
On MacBook Pro M2 Pro 32GB, Helply 10.2b chat i1 can safely use up to 167K 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--helply-10-2b-chat-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:
5.7 GB |
| Medium |
| C46 |
Q4_K_M | 4 | 6.2 GB | Medium | C46 |
Q5_K_M | 5 | 7.3 GB | High | C47 |
Q6_K | 6 | 8.4 GB | High | C47 |
Q8_0Best for your GPU | 8 | 10.9 GB | Very High | C49 |
F16 | 16 | 20.9 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.