MacBook Pro M2 Max 32GBBudget pick
32 GB Unified (+8)400 GB/s (+127)
C
Adds memory headroom for longer context windows and future model growth.25.4 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
HelpingAI 15B i1 needs ~14.4 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~23 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
Tight fit
Decode
21.2 tok/s
TTFT
9120 ms
Safe context
42K
Memory
14.4 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 | 23.0 tok/s | 4597 ms | 42K |
| Coding | C | Tight fit | 23.0 tok/s | 8427 ms | 42K |
| Agentic Coding | C | Tight fit | 23.0 tok/s | 12257 ms | 42K |
| Reasoning | C | Tight fit | 23.0 tok/s | 9959 ms | 42K |
| RAG | C | Tight fit | 23.0 tok/s | 15322 ms | 42K |
How HelpingAI 15B i1 (15B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C48 |
Q3_K_S | 3 | 7.4 GB | Low | C50 |
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
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yes, MacBook Pro M4 Pro 24GB can run HelpingAI 15B i1 with a C grade (Tight fit). Expected decode speed: 23.0 tok/s.
HelpingAI 15B i1 (15B parameters) requires approximately 14.4 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 M4 Pro 24GB, HelpingAI 15B i1 achieves approximately 23.0 tokens per second decode speed with a time-to-first-token of 8427ms using Q4_K_M quantization.
For coding workloads, HelpingAI 15B i1 on MacBook Pro M4 Pro 24GB receives a C grade with 23.0 tok/s and 42K context.
On MacBook Pro M4 Pro 24GB, HelpingAI 15B i1 can safely use up to 42K 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-m4-pro-24gb" 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 |
| C51 |
Q4_K_M | 4 | 9.2 GB | Medium | C51 |
Q5_K_M | 5 | 10.8 GB | High | C50 |
Q6_KBest for your GPU | 6 | 12.3 GB | High | C50 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Not always. MacBook Pro M4 Pro 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.