RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick1792 GB/s (+992)
C
Raises estimated decode speed by about 242%.164.5 tok/s decode
Raises estimated decode speed by about 242%.
~$9,999 MSRP
HelpingAI 15B i1 needs ~25.6 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~48 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
48.1 tok/s
TTFT
4026 ms
Safe context
622K
Memory
25.6 GB / 92.2 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 | 48.1 tok/s | 2196 ms | 622K |
| Coding | C | Runs well | 48.1 tok/s | 4026 ms | 622K |
| Agentic Coding | C | Runs well | 48.1 tok/s | 5856 ms | 622K |
| Reasoning | C | Runs well | 48.1 tok/s | 4758 ms | 622K |
| RAG | C | Runs well | 48.1 tok/s | 7320 ms | 622K |
How HelpingAI 15B i1 (15B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | D39 |
Q3_K_S | 3 | 7.4 GB | Low | D39 |
NVFP4 | 4 | 8.4 GB | Medium | D39 |
Q4_K_M | 4 | 9.2 GB | Medium | D39 |
Q5_K_M | 5 | 10.8 GB | High | D39 |
Q6_K | 6 | 12.3 GB | High | D39 |
Q8_0 | 8 | 16.1 GB | Very High | D40 |
F16Best for your GPU | 16 | 30.7 GB | Maximum | C42 |
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 6000 Blackwell Workstation Edition 96GBBudget pickRaises estimated decode speed by about 242%.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueRaises estimated decode speed by about 205%.
~$9,999 MSRP
Yes, Mac Studio M1 Ultra 128GB can run HelpingAI 15B i1 with a C grade (Runs well). Expected decode speed: 48.1 tok/s.
HelpingAI 15B i1 (15B parameters) requires approximately 25.6 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 M1 Ultra 128GB, HelpingAI 15B i1 achieves approximately 48.1 tokens per second decode speed with a time-to-first-token of 4026ms using Q4_K_M quantization.
For coding workloads, HelpingAI 15B i1 on Mac Studio M1 Ultra 128GB receives a C grade with 48.1 tok/s and 622K context.
On Mac Studio M1 Ultra 128GB, HelpingAI 15B i1 can safely use up to 622K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. Mac Studio M1 Ultra 128GB 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-m1-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: