MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+12)
C
This setup is broadly balanced for this model.76.9 tok/s decode
~$2,499 MSRP
HelpingAI2 6B needs ~8.0 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 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
381K
Memory
8.0 GB / 24.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 84.0 tok/s | 1257 ms | 381K |
| Coding | C | Runs well | 84.0 tok/s | 2305 ms | 381K |
| Agentic Coding | C | Runs well | 84.0 tok/s | 3352 ms | 381K |
| Reasoning | C | Runs well | 84.0 tok/s | 2724 ms | 381K |
| RAG | C | Runs well | 84.0 tok/s | 4190 ms | 381K |
How HelpingAI2 6B (6B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C44 |
Q3_K_S | 3 | 2.9 GB | Low | C44 |
NVFP4 | 4 | 3.4 GB | Medium | C44 |
Q4_K_M | 4 | 3.7 GB | Medium | C44 |
Q5_K_M | 5 | 4.3 GB | High | C45 |
Q6_K | 6 | 4.9 GB | High | C45 |
Q8_0 | 8 | 6.4 GB | Very High | C46 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
Copy-paste commands to run HelpingAI2 6B on your machine.
Run
lms load hf-helpingai--helpingai2-6b && lms server startUpgrade options
Yes, RTX PRO 4000 Blackwell 24GB can run HelpingAI2 6B with a C grade (Runs well). Expected decode speed: 84.0 tok/s.
HelpingAI2 6B (6B parameters) requires approximately 8.0 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 RTX PRO 4000 Blackwell 24GB, 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 RTX PRO 4000 Blackwell 24GB receives a C grade with 84.0 tok/s and 381K context.
On RTX PRO 4000 Blackwell 24GB, HelpingAI2 6B can safely use up to 381K 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-rtx-pro-4000-blackwell-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: