RTX PRO 5000 Blackwell 48GBBudget pick1344 GB/s (+544)
C
Raises estimated decode speed by about 143%.123.4 tok/s decode
Raises estimated decode speed by about 143%.
~$4,999 MSRP
HelpingAI 15B i1 needs ~18.7 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~51 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
50.7 tok/s
TTFT
3818 ms
Safe context
265K
Memory
18.7 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 | 50.7 tok/s | 2082 ms | 265K |
| Coding | C | Runs well | 50.7 tok/s | 3818 ms | 265K |
| Agentic Coding | C | Runs well | 50.7 tok/s | 5553 ms | 265K |
| Reasoning | C | Runs well | 50.7 tok/s | 4512 ms | 265K |
| RAG | C | Runs well | 50.7 tok/s | 6941 ms | 265K |
How HelpingAI 15B i1 (15B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C42 |
Q3_K_S | 3 | 7.4 GB | Low | C42 |
NVFP4 | 4 | 8.4 GB | Medium | C42 |
Q4_K_M | 4 | 9.2 GB | Medium | C42 |
Q5_K_M | 5 | 10.8 GB | High | C43 |
Q6_K | 6 | 12.3 GB | High | C43 |
Q8_0 | 8 | 16.1 GB | Very High | C45 |
F16Best for your GPU | 16 | 30.7 GB | Maximum | C48 |
Copy-paste commands to run HelpingAI 15B i1 on your machine.
Run
lms load hf-mradermacher--helpingai-15b-i1-gguf && lms server startUpgrade options
RTX PRO 5000 Blackwell 48GBBudget pickRaises estimated decode speed by about 143%.
~$4,999 MSRP
NVIDIA L40 48GBBest valueRaises estimated decode speed by about 49%.
~$5,500 MSRP
Yes, Mac Studio M2 Ultra 64GB can run HelpingAI 15B i1 with a C grade (Runs well). Expected decode speed: 50.7 tok/s.
HelpingAI 15B i1 (15B parameters) requires approximately 18.7 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 15B i1 is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M2 Ultra 64GB, HelpingAI 15B i1 achieves approximately 50.7 tokens per second decode speed with a time-to-first-token of 3818ms using Q4_K_M quantization.
For coding workloads, HelpingAI 15B i1 on Mac Studio M2 Ultra 64GB receives a C grade with 50.7 tok/s and 265K context.
On Mac Studio M2 Ultra 64GB, HelpingAI 15B i1 can safely use up to 265K 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--helpingai-15b-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>
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