HelpingAI2 6B i1 needs ~7.9 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~19 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
18.6 tok/s
TTFT
10420 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 | 18.6 tok/s | 5684 ms | 230K |
| Coding | C | Runs well | 18.6 tok/s | 10420 ms | 230K |
| Agentic Coding | C | Runs well | 18.6 tok/s | 15157 ms | 230K |
| Reasoning | C | Runs well | 18.6 tok/s | 12315 ms | 230K |
| RAG | C | Runs well | 18.6 tok/s | 18946 ms | 230K |
How HelpingAI2 6B i1 (6B params) fits at each quantization level on MacBook Pro M3 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 i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-6b-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 241%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 106%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 91%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yes, MacBook Pro M3 24GB can run HelpingAI2 6B i1 with a C grade (Runs well). Expected decode speed: 18.6 tok/s.
HelpingAI2 6B i1 (6B parameters) requires approximately 7.9 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 M3 24GB, HelpingAI2 6B i1 achieves approximately 18.6 tokens per second decode speed with a time-to-first-token of 10420ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 6B i1 on MacBook Pro M3 24GB receives a C grade with 18.6 tok/s and 230K context.
On MacBook Pro M3 24GB, HelpingAI2 6B i1 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-mradermacher--helpingai2-6b-i1-gguf-on-m3-24gb" 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 | C46 |
Q5_K_M | 5 | 4.3 GB | High | C47 |
Q6_K | 6 | 4.9 GB | High | C47 |
Q8_0 | 8 | 6.4 GB | Very High | C49 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
Not always. MacBook Pro M3 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.