HelpingAI2.5 10B i1 needs ~15.1 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~76 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
76.1 tok/s
TTFT
2545 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 | 76.1 tok/s | 1388 ms | 439K |
| Coding | C | Runs well | 76.1 tok/s | 2545 ms | 439K |
| Agentic Coding | C | Runs well | 76.1 tok/s | 3702 ms | 439K |
| Reasoning | C | Runs well | 76.1 tok/s | 3008 ms | 439K |
| RAG | C | Runs well | 76.1 tok/s | 4628 ms | 439K |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on Mac Studio M2 Ultra 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 startYes, Mac Studio M2 Ultra 64GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 76.1 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 Studio M2 Ultra 64GB, HelpingAI2.5 10B i1 achieves approximately 76.1 tokens per second decode speed with a time-to-first-token of 2545ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on Mac Studio M2 Ultra 64GB receives a C grade with 76.1 tok/s and 439K context.
On Mac Studio M2 Ultra 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 Studio M2 Ultra 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-m2-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: