Helply 10.2b chat i1 needs ~10.0 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
Tight fit
Decode
6.6 tok/s
TTFT
29524 ms
Safe context
36K
Memory
10.0 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 | Tight fit | 6.6 tok/s | 16104 ms | 36K |
| Coding | C | Tight fit | 6.6 tok/s | 29524 ms | 36K |
| Agentic Coding | C | Runs with offload | 6.6 tok/s | 42944 ms | 36K |
| Reasoning | C | Tight fit | 6.6 tok/s | 34892 ms | 36K |
| RAG | C | Runs with offload | 6.6 tok/s | 53680 ms | 36K |
How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on MacBook Air M1 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.0 GB | Low | C51 |
Q3_K_S | 3 | 5.0 GB | Low | C52 |
NVFP4 | 4 |
Copy-paste commands to run Helply 10.2b chat i1 on your machine.
Run
lms load hf-mradermacher--helply-10-2b-chat-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 85%.
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Raises estimated decode speed by about 85%.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Raises estimated decode speed by about 65%.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
RTX 3080 Ti 12GBBiggest leapRaises estimated decode speed by about 1511%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$1,199 MSRP
Yes, MacBook Air M1 16GB can run Helply 10.2b chat i1 with a C grade (Tight fit). Expected decode speed: 6.6 tok/s.
Helply 10.2b chat i1 (10.199999809265137B parameters) requires approximately 10.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Helply 10.2b chat i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M1 16GB, Helply 10.2b chat i1 achieves approximately 6.6 tokens per second decode speed with a time-to-first-token of 29524ms using Q4_K_M quantization.
For coding workloads, Helply 10.2b chat i1 on MacBook Air M1 16GB receives a C grade with 6.6 tok/s and 36K context.
On MacBook Air M1 16GB, Helply 10.2b chat i1 can safely use up to 36K 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--helply-10-2b-chat-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:
5.7 GB |
| Medium |
| C52 |
Q4_K_M | 4 | 6.2 GB | Medium | C52 |
Q5_K_M | 5 | 7.3 GB | High | C51 |
Q6_KBest for your GPU | 6 | 8.4 GB | High | C51 |
Q8_0 | 8 | 10.9 GB | Very High | F0 |
F16 | 16 | 20.9 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.