MacBook Pro M3 Pro 18GBBudget pick
18 GB Unified (+2)150 GB/s (+82)
C
Raises estimated decode speed by about 169%.19.9 tok/s decode
Raises estimated decode speed by about 169%.
~$1,999 MSRP
HelpingAI 9B i1 needs ~9.2 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~7 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
7.4 tok/s
TTFT
26051 ms
Safe context
52K
Memory
9.2 GB / 11.5 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 7.4 tok/s | 14209 ms | 52K |
| Coding | C | Runs well | 7.4 tok/s | 26051 ms | 52K |
| Agentic Coding | C | Tight fit | 7.4 tok/s | 37892 ms | 52K |
| Reasoning | C | Runs well | 7.4 tok/s | 30787 ms | 52K |
| RAG | C | Tight fit | 7.4 tok/s | 47365 ms | 52K |
How HelpingAI 9B i1 (9B params) fits at each quantization level on MacBook Air M1 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 169%.
~$1,999 MSRP
Raises estimated decode speed by about 376%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 472%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yes, MacBook Air M1 16GB can run HelpingAI 9B i1 with a C grade (Runs well). Expected decode speed: 7.4 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 Air M1 16GB, HelpingAI 9B i1 achieves approximately 7.4 tokens per second decode speed with a time-to-first-token of 26051ms using Q4_K_M quantization.
For coding workloads, HelpingAI 9B i1 on MacBook Air M1 16GB receives a C grade with 7.4 tok/s and 52K context.
On MacBook Air M1 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-m1-16gb" 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 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. MacBook Air M1 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.