HelpingAI2 6B needs ~8.7 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~22 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
21.7 tok/s
TTFT
8914 ms
Safe context
342K
Memory
8.7 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 | 21.7 tok/s | 4862 ms | 342K |
| Coding | C | Runs well | 21.7 tok/s | 8914 ms | 342K |
| Agentic Coding | C | Runs well | 21.7 tok/s | 12966 ms | 342K |
| Reasoning | C | Runs well | 21.7 tok/s | 10535 ms | 342K |
| RAG | C | Runs well | 21.7 tok/s | 16208 ms | 342K |
How HelpingAI2 6B (6B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C44 |
Q3_K_S | 3 | 2.9 GB | Low | C44 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2 6B on your machine.
Run
lms load hf-helpingai--helpingai2-6b && lms server startUpgrade options
Raises estimated decode speed by about 143%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Raises estimated decode speed by about 38%.
~$1,999 MSRP
Raises estimated decode speed by about 287%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 32GB can run HelpingAI2 6B with a C grade (Runs well). Expected decode speed: 21.7 tok/s.
HelpingAI2 6B (6B parameters) requires approximately 8.7 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 Mac mini M4 32GB, HelpingAI2 6B achieves approximately 21.7 tokens per second decode speed with a time-to-first-token of 8914ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 6B on Mac mini M4 32GB receives a C grade with 21.7 tok/s and 342K context.
On Mac mini M4 32GB, HelpingAI2 6B can safely use up to 342K 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-m4-mini-32gb" 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 |
| C44 |
Q4_K_M | 4 | 3.7 GB | Medium | C45 |
Q5_K_M | 5 | 4.3 GB | High | C45 |
Q6_K | 6 | 4.9 GB | High | C45 |
Q8_0 | 8 | 6.4 GB | Very High | C46 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
Not always. Mac mini M4 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.