MacBook Pro M3 Pro 18GBBudget pick
18 GB Unified (+2)150 GB/s (+30)
C
Raises estimated decode speed by about 37%.19.9 tok/s decode
Raises estimated decode speed by about 37%.
~$1,999 MSRP
HelpingAI 9B i1 needs ~9.2 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~16 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
14.5 tok/s
TTFT
13371 ms
Safe context
52K
Memory
9.2 GB / 11.5 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 | 14.5 tok/s | 7293 ms | 52K |
| Coding | C | Runs well | 15.7 tok/s | 12302 ms | 52K |
| Agentic Coding | C | Tight fit | 14.5 tok/s | 19449 ms | 52K |
| Reasoning | C | Runs well | 14.5 tok/s | 15803 ms | 52K |
| RAG | C | Tight fit | 14.5 tok/s | 24312 ms | 52K |
How HelpingAI 9B i1 (9B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C50 |
Q3_K_S | 3 | 4.4 GB | Low | C51 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI 9B i1 on your machine.
Run
lms load hf-mradermacher--helpingai-9b-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 37%.
~$1,999 MSRP
Raises estimated decode speed by about 143%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yes, MacBook Pro M4 16GB can run HelpingAI 9B i1 with a C grade (Runs well). Expected decode speed: 15.7 tok/s.
HelpingAI 9B i1 (9B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 9B i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 16GB, HelpingAI 9B i1 achieves approximately 15.7 tokens per second decode speed with a time-to-first-token of 12302ms using Q4_K_M quantization.
For coding workloads, HelpingAI 9B i1 on MacBook Pro M4 16GB receives a C grade with 15.7 tok/s and 52K context.
On MacBook Pro M4 16GB, HelpingAI 9B i1 can safely use up to 52K 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-9b-i1-gguf-on-m4-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
5.0 GB |
| Medium |
| C52 |
Q4_K_M | 4 | 5.5 GB | Medium | C52 |
Q5_K_M | 5 | 6.5 GB | High | C52 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | C51 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Not always. MacBook Pro M4 16GB 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.