HelpingAI2 9B needs ~35.1 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~101 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
101.4 tok/s
TTFT
1908 ms
Safe context
2.3M
Memory
35.1 GB / 184.3 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 | 101.4 tok/s | 1041 ms | 2.3M |
| Coding | C | Runs well | 101.4 tok/s | 1908 ms | 2.3M |
| Agentic Coding | C | Runs well | 101.4 tok/s | 2776 ms | 2.3M |
| Reasoning | C | Runs well | 101.4 tok/s | 2255 ms | 2.3M |
| RAG | C | Runs well | 101.4 tok/s | 3470 ms | 2.3M |
How HelpingAI2 9B (9B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | D37 |
Q3_K_S | 3 | 4.4 GB | Low | D37 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2 9B on your machine.
Run
lms load hf-bartowski--helpingai2-9b-gguf && lms server startYes, Mac Studio M3 Ultra 256GB can run HelpingAI2 9B with a C grade (Runs well). Expected decode speed: 101.4 tok/s.
HelpingAI2 9B (9B parameters) requires approximately 35.1 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2 9B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M3 Ultra 256GB, HelpingAI2 9B achieves approximately 101.4 tokens per second decode speed with a time-to-first-token of 1908ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 9B on Mac Studio M3 Ultra 256GB receives a C grade with 101.4 tok/s and 2.3M context.
On Mac Studio M3 Ultra 256GB, HelpingAI2 9B can safely use up to 2.3M 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-bartowski--helpingai2-9b-gguf-on-m3-ultra-256gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
5.0 GB |
| Medium |
| D37 |
Q4_K_M | 4 | 5.5 GB | Medium | D37 |
Q5_K_M | 5 | 6.5 GB | High | D37 |
Q6_K | 6 | 7.4 GB | High | D37 |
Q8_0 | 8 | 9.6 GB | Very High | D37 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | D37 |
Not always. Mac Studio M3 Ultra 256GB 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.