HelpingAI 15B i1 needs ~17.0 GB VRAM. MacBook Pro M3 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~26 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
26.2 tok/s
TTFT
7381 ms
Safe context
176K
Memory
17.0 GB / 34.6 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 | 26.2 tok/s | 4026 ms | 176K |
| Coding | C | Runs well | 26.2 tok/s | 7381 ms | 176K |
| Agentic Coding | C | Runs well | 26.2 tok/s | 10736 ms | 176K |
| Reasoning | C | Runs well | 26.2 tok/s | 8723 ms | 176K |
| RAG | C | Runs well | 26.2 tok/s | 13420 ms | 176K |
How HelpingAI 15B i1 (15B params) fits at each quantization level on MacBook Pro M3 Max 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C43 |
Q3_K_S | 3 | 7.4 GB | Low | C44 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI 15B i1 on your machine.
Run
lms load hf-mradermacher--helpingai-15b-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 94%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 84%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M3 Max 48GB can run HelpingAI 15B i1 with a C grade (Runs well). Expected decode speed: 26.2 tok/s.
HelpingAI 15B i1 (15B parameters) requires approximately 17.0 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 15B i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Max 48GB, HelpingAI 15B i1 achieves approximately 26.2 tokens per second decode speed with a time-to-first-token of 7381ms using Q4_K_M quantization.
For coding workloads, HelpingAI 15B i1 on MacBook Pro M3 Max 48GB receives a C grade with 26.2 tok/s and 176K context.
On MacBook Pro M3 Max 48GB, HelpingAI 15B i1 can safely use up to 176K 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--helpingai-15b-i1-gguf-on-m3-max-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
8.4 GB |
| Medium |
| C44 |
Q4_K_M | 4 | 9.2 GB | Medium | C44 |
Q5_K_M | 5 | 10.8 GB | High | C45 |
Q6_K | 6 | 12.3 GB | High | C46 |
Q8_0Best for your GPU | 8 | 16.1 GB | Very High | C47 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Not always. MacBook Pro M3 Max 48GB 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.