HelpingAI2 6B i1 needs ~15.6 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~63 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
63.4 tok/s
TTFT
3054 ms
Safe context
1.2M
Memory
15.6 GB / 69.1 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 | 63.4 tok/s | 1666 ms | 1.2M |
| Coding | C | Runs well | 63.4 tok/s | 3054 ms | 1.2M |
| Agentic Coding | C | Runs well | 63.4 tok/s | 4442 ms | 1.2M |
| Reasoning | C | Runs well | 63.4 tok/s | 3610 ms | 1.2M |
| RAG | C | Runs well | 63.4 tok/s | 5553 ms | 1.2M |
How HelpingAI2 6B i1 (6B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | D40 |
Q3_K_S | 3 | 2.9 GB | Low | D40 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2 6B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-6b-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 32%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 32%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 32%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
Yes, MacBook Pro M2 Max 96GB can run HelpingAI2 6B i1 with a C grade (Runs well). Expected decode speed: 63.4 tok/s.
HelpingAI2 6B i1 (6B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2 6B i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Max 96GB, HelpingAI2 6B i1 achieves approximately 63.4 tokens per second decode speed with a time-to-first-token of 3054ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 6B i1 on MacBook Pro M2 Max 96GB receives a C grade with 63.4 tok/s and 1.2M context.
On MacBook Pro M2 Max 96GB, HelpingAI2 6B i1 can safely use up to 1.2M 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--helpingai2-6b-i1-gguf-on-m2-max-96gb" 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 |
| D40 |
Q4_K_M | 4 | 3.7 GB | Medium | D40 |
Q5_K_M | 5 | 4.3 GB | High | D40 |
Q6_K | 6 | 4.9 GB | High | D40 |
Q8_0 | 8 | 6.4 GB | Very High | D40 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C41 |
Not always. MacBook Pro M2 Max 96GB 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.