HelpingAI2.5 5B i1 needs ~11.4 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~26 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
26.1 tok/s
TTFT
7429 ms
Safe context
962K
Memory
11.4 GB / 46.1 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 | 26.1 tok/s | 4052 ms | 962K |
| Coding | C | Runs well | 26.1 tok/s | 7429 ms | 962K |
| Agentic Coding | C | Runs well | 26.1 tok/s | 10805 ms | 962K |
| Reasoning | C | Runs well | 26.1 tok/s | 8779 ms | 962K |
| RAG | C | Runs well | 26.1 tok/s | 13506 ms | 962K |
How HelpingAI2.5 5B i1 (5B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.0 GB | Low | C41 |
Q3_K_S | 3 | 2.5 GB | Low | C41 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2.5 5B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-5b-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 168%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 168%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 168%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 64GB can run HelpingAI2.5 5B i1 with a C grade (Runs well). Expected decode speed: 26.1 tok/s.
HelpingAI2.5 5B i1 (5B parameters) requires approximately 11.4 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2.5 5B i1 is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M4 64GB, HelpingAI2.5 5B i1 achieves approximately 26.1 tokens per second decode speed with a time-to-first-token of 7429ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 5B i1 on Mac mini M4 64GB receives a C grade with 26.1 tok/s and 962K context.
On Mac mini M4 64GB, HelpingAI2.5 5B i1 can safely use up to 962K 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--helpingai2-5-5b-i1-gguf-on-m4-mini-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
2.8 GB |
| Medium |
| C41 |
Q4_K_M | 4 | 3.1 GB | Medium | C41 |
Q5_K_M | 5 | 3.6 GB | High | C41 |
Q6_K | 6 | 4.1 GB | High | C41 |
Q8_0 | 8 | 5.4 GB | Very High | C41 |
F16Best for your GPU | 16 | 10.3 GB | Maximum | C43 |
Not always. Mac mini M4 64GB 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.