MacBook Pro M3 Pro 36GBBudget pick
36 GB Unified (+4)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
HelpingAI2 9B needs ~10.9 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~15 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
200K
Memory
10.9 GB / 23.0 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 | 200K |
| Coding | C | Runs well | 14.5 tok/s | 13371 ms | 200K |
| Agentic Coding | C | Runs well | 14.5 tok/s | 19449 ms | 200K |
| Reasoning | C | Runs well | 14.5 tok/s | 15803 ms | 200K |
| RAG | C | Runs well | 14.5 tok/s | 24312 ms | 200K |
How HelpingAI2 9B (9B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C44 |
Q3_K_S | 3 | 4.4 GB | Low | C45 |
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 37%.
~$1,999 MSRP
Raises estimated decode speed by about 254%.
~$2,499 MSRP
Raises estimated decode speed by about 483%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 32GB can run HelpingAI2 9B with a C grade (Runs well). Expected decode speed: 14.5 tok/s.
HelpingAI2 9B (9B parameters) requires approximately 10.9 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 M4 32GB, HelpingAI2 9B achieves approximately 14.5 tokens per second decode speed with a time-to-first-token of 13371ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 9B on Mac mini M4 32GB receives a C grade with 14.5 tok/s and 200K context.
On Mac mini M4 32GB, HelpingAI2 9B can safely use up to 200K 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-m4-mini-32gb" 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 |
| C45 |
Q4_K_M | 4 | 5.5 GB | Medium | C46 |
Q5_K_M | 5 | 6.5 GB | High | C46 |
Q6_K | 6 | 7.4 GB | High | C47 |
Q8_0 | 8 | 9.6 GB | Very High | C48 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C49 |
Not always. Mac mini M4 32GB 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.