HelpingAI2 6B needs ~32.9 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~84 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
84.0 tok/s
TTFT
2305 ms
Safe context
3.5M
Memory
32.9 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 | 84.0 tok/s | 1257 ms | 3.5M |
| Coding | C | Runs well | 84.0 tok/s | 2305 ms | 3.5M |
| Agentic Coding | C | Runs well | 84.0 tok/s | 3352 ms | 3.5M |
| Reasoning | C | Runs well | 84.0 tok/s | 2724 ms | 3.5M |
| RAG | C | Runs well | 84.0 tok/s | 4190 ms | 3.5M |
How HelpingAI2 6B (6B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | D37 |
Q3_K_S | 3 | 2.9 GB | Low | D37 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2 6B on your machine.
Run
lms load hf-helpingai--helpingai2-6b && lms server startYes, Mac Studio M3 Ultra 256GB can run HelpingAI2 6B with a C grade (Runs well). Expected decode speed: 84.0 tok/s.
HelpingAI2 6B (6B parameters) requires approximately 32.9 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2 6B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M3 Ultra 256GB, HelpingAI2 6B achieves approximately 84.0 tokens per second decode speed with a time-to-first-token of 2305ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 6B on Mac Studio M3 Ultra 256GB receives a C grade with 84.0 tok/s and 3.5M context.
On Mac Studio M3 Ultra 256GB, HelpingAI2 6B can safely use up to 3.5M 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-helpingai--helpingai2-6b-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:
3.4 GB |
| Medium |
| D37 |
Q4_K_M | 4 | 3.7 GB | Medium | D37 |
Q5_K_M | 5 | 4.3 GB | High | D37 |
Q6_K | 6 | 4.9 GB | High | D37 |
Q8_0 | 8 | 6.4 GB | Very High | D37 |
F16Best for your GPU | 16 | 12.3 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.