Intel Arc B580 12GBBudget pick456 GB/s (+256)
C
Raises estimated decode speed by about 56%.59.8 tok/s decode
Raises estimated decode speed by about 56%.
~$249 MSRP
HelpingAI2 6B needs ~7.0 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~38 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
38.3 tok/s
TTFT
5061 ms
Safe context
119K
Memory
7.0 GB / 11.5 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 | 38.3 tok/s | 2761 ms | 119K |
| Coding | C | Runs well | 38.3 tok/s | 5061 ms | 119K |
| Agentic Coding | C | Runs well | 38.3 tok/s | 7362 ms | 119K |
| Reasoning | C | Runs well | 38.3 tok/s | 5981 ms | 119K |
| RAG | C | Runs well | 38.3 tok/s | 9202 ms | 119K |
How HelpingAI2 6B (6B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C48 |
Q3_K_S | 3 | 2.9 GB | Low | C49 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2 6B on your machine.
Run
lms load hf-helpingai--helpingai2-6b && lms server startUpgrade options
Intel Arc B580 12GBBudget pickRaises estimated decode speed by about 56%.
~$249 MSRP
Raises estimated decode speed by about 85%.
~$449 MSRP
Yes, MacBook Pro M2 Pro 16GB can run HelpingAI2 6B with a C grade (Runs well). Expected decode speed: 38.3 tok/s.
HelpingAI2 6B (6B parameters) requires approximately 7.0 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2 6B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, HelpingAI2 6B achieves approximately 38.3 tokens per second decode speed with a time-to-first-token of 5061ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 6B on MacBook Pro M2 Pro 16GB receives a C grade with 38.3 tok/s and 119K context.
On MacBook Pro M2 Pro 16GB, HelpingAI2 6B can safely use up to 119K 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-helpingai--helpingai2-6b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.4 GB |
| Medium |
| C50 |
Q4_K_M | 4 | 3.7 GB | Medium | C50 |
Q5_K_M | 5 | 4.3 GB | High | C51 |
Q6_K | 6 | 4.9 GB | High | C52 |
Q8_0Best for your GPU | 8 | 6.4 GB | Very High | C52 |
F16 | 16 | 12.3 GB | Maximum | F0 |
Not always. MacBook Pro M2 Pro 16GB 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.