MacBook Pro M4 32GBBudget pick
32 GB Unified (+8)
C
Adds memory headroom for longer context windows and future model growth.8.7 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
HelpingAI 15B i1 needs ~14.4 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~9 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
8.7 tok/s
TTFT
22189 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 | 8.7 tok/s | 12103 ms | 42K |
| Coding | C | Tight fit | 8.7 tok/s | 22189 ms | 42K |
| Agentic Coding | C | Tight fit | 8.7 tok/s | 32275 ms | 42K |
| Reasoning | C | Tight fit | 8.7 tok/s | 26224 ms | 42K |
| RAG | C | Tight fit | 8.7 tok/s | 40344 ms | 42K |
How HelpingAI 15B i1 (15B params) fits at each quantization level on MacBook Air M4 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.
~$799 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Raises estimated decode speed by about 192%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yes, MacBook Air M4 24GB can run HelpingAI 15B i1 with a C grade (Tight fit). Expected decode speed: 8.7 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 Air M4 24GB, HelpingAI 15B i1 achieves approximately 8.7 tokens per second decode speed with a time-to-first-token of 22189ms using Q4_K_M quantization.
For coding workloads, HelpingAI 15B i1 on MacBook Air M4 24GB receives a C grade with 8.7 tok/s and 42K context.
On MacBook Air M4 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-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:
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 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.