HelpingAI2 6B needs ~7.9 GB VRAM. MacBook Air M4 24GB has 17.3 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
230K
Memory
7.9 GB / 17.3 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 | 230K |
| Coding | C | Runs well | 21.7 tok/s | 8914 ms | 230K |
| Agentic Coding | C | Runs well | 21.7 tok/s | 12966 ms | 230K |
| Reasoning | C | Runs well | 21.7 tok/s | 10535 ms | 230K |
| RAG | C | Runs well | 21.7 tok/s | 16208 ms | 230K |
How HelpingAI2 6B (6B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C45 |
Q3_K_S | 3 | 2.9 GB | Low | C46 |
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 192%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 76%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 254%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Air M4 24GB can run HelpingAI2 6B with a C grade (Runs well). Expected decode speed: 21.7 tok/s.
HelpingAI2 6B (6B parameters) requires approximately 7.9 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 Air M4 24GB, 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 MacBook Air M4 24GB receives a C grade with 21.7 tok/s and 230K context.
On MacBook Air M4 24GB, HelpingAI2 6B can safely use up to 230K 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-air-24gb" 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 |
| C46 |
Q4_K_M | 4 | 3.7 GB | Medium | C47 |
Q5_K_M | 5 | 4.3 GB | High | C47 |
Q6_K | 6 | 4.9 GB | High | C48 |
Q8_0 | 8 | 6.4 GB | Very High | C49 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
Not always. MacBook Air M4 24GB 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.