HelpingAI2 9B needs ~10.0 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~12 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
11.8 tok/s
TTFT
16352 ms
Safe context
126K
Memory
10.0 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 | 11.8 tok/s | 8919 ms | 126K |
| Coding | C | Runs well | 11.8 tok/s | 16352 ms | 126K |
| Agentic Coding | C | Runs well | 11.8 tok/s | 23784 ms | 126K |
| Reasoning | C | Runs well | 11.8 tok/s | 19325 ms | 126K |
| RAG | C | Runs well | 11.8 tok/s | 29730 ms | 126K |
How HelpingAI2 9B (9B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C46 |
Q3_K_S | 3 | 4.4 GB | Low | C47 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2 9B on your machine.
Run
lms load hf-bartowski--helpingai2-9b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 258%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 116%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 616%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M2 24GB can run HelpingAI2 9B with a C grade (Runs well). Expected decode speed: 11.8 tok/s.
HelpingAI2 9B (9B parameters) requires approximately 10.0 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2 9B is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M2 24GB, HelpingAI2 9B achieves approximately 11.8 tokens per second decode speed with a time-to-first-token of 16352ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 9B on Mac mini M2 24GB receives a C grade with 11.8 tok/s and 126K context.
On Mac mini M2 24GB, HelpingAI2 9B can safely use up to 126K 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-bartowski--helpingai2-9b-gguf-on-m2-24gb" 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 |
| C48 |
Q4_K_M | 4 | 5.5 GB | Medium | C48 |
Q5_K_M | 5 | 6.5 GB | High | C49 |
Q6_K | 6 | 7.4 GB | High | C50 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | C51 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Not always. Mac mini M2 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.