MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+16)
C
This setup is broadly balanced for this model.68.3 tok/s decode
~$2,499 MSRP
HelpingAI2 9B needs ~10.6 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~62 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
61.9 tok/s
TTFT
3128 ms
Safe context
340K
Memory
10.6 GB / 32.0 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 | 61.9 tok/s | 1706 ms | 340K |
| Coding | C | Runs well | 61.9 tok/s | 3128 ms | 340K |
| Agentic Coding | C | Runs well | 61.9 tok/s | 4549 ms | 340K |
| Reasoning | C | Runs well | 61.9 tok/s | 3696 ms | 340K |
| RAG | C | Runs well | 61.9 tok/s | 5686 ms | 340K |
How HelpingAI2 9B (9B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C43 |
Q3_K_S | 3 | 4.4 GB | Low | C43 |
NVFP4 | 4 | 5.0 GB | Medium | C43 |
Q4_K_M | 4 | 5.5 GB | Medium | C43 |
Q5_K_M | 5 | 6.5 GB | High | C44 |
Q6_K | 6 | 7.4 GB | High | C44 |
Q8_0 | 8 | 9.6 GB | Very High | C45 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C49 |
Copy-paste commands to run HelpingAI2 9B on your machine.
Run
lms load hf-bartowski--helpingai2-9b-gguf && lms server startUpgrade options
~$2,499 MSRP
Raises estimated decode speed by about 37%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Radeon Pro W7800 32GB can run HelpingAI2 9B with a C grade (Runs well). Expected decode speed: 61.9 tok/s.
HelpingAI2 9B (9B parameters) requires approximately 10.6 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 Radeon Pro W7800 32GB, HelpingAI2 9B achieves approximately 61.9 tokens per second decode speed with a time-to-first-token of 3128ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 9B on Radeon Pro W7800 32GB receives a C grade with 61.9 tok/s and 340K context.
On Radeon Pro W7800 32GB, HelpingAI2 9B can safely use up to 340K 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-radeon-pro-w7800-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: