HelpingAI2.5 10B i1 needs ~15.1 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~13 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
13.0 tok/s
TTFT
14857 ms
Safe context
439K
Memory
15.1 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 | 13.0 tok/s | 8104 ms | 439K |
| Coding | C | Runs well | 13.0 tok/s | 14857 ms | 439K |
| Agentic Coding | C | Runs well | 13.0 tok/s | 21610 ms | 439K |
| Reasoning | C | Runs well | 13.0 tok/s | 17558 ms | 439K |
| RAG | C | Runs well | 13.0 tok/s | 27013 ms | 439K |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C41 |
Q3_K_S | 3 | 4.9 GB | Low | C41 |
NVFP4 | 4 | 5.6 GB | Medium | C41 |
Q4_K_M | 4 | 6.1 GB | Medium | C42 |
Q5_K_M | 5 | 7.2 GB | High | C42 |
Q6_K | 6 | 8.2 GB | High | C42 |
Q8_0 | 8 | 10.7 GB | Very High | C43 |
F16Best for your GPU | 16 | 20.5 GB | Maximum | C46 |
Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server start升级选项
Raises estimated decode speed by about 373%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 202%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 602%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 64GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 13.0 tok/s.
HelpingAI2.5 10B i1 (10B parameters) requires approximately 15.1 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M4 64GB, HelpingAI2.5 10B i1 achieves approximately 13.0 tokens per second decode speed with a time-to-first-token of 14857ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on Mac mini M4 64GB receives a C grade with 13.0 tok/s and 439K context.
On Mac mini M4 64GB, HelpingAI2.5 10B i1 can safely use up to 439K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai2-5-10b-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>
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