HelpingAI 9B 200k i1 needs ~11.3 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~20 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
19.9 tok/s
TTFT
9707 ms
Safe context
237K
Memory
11.3 GB / 25.9 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 | 19.9 tok/s | 5294 ms | 237K |
| Coding | C | Runs well | 19.9 tok/s | 9707 ms | 237K |
| Agentic Coding | C | Runs well | 19.9 tok/s | 14119 ms | 237K |
| Reasoning | C | Runs well | 19.9 tok/s | 11471 ms | 237K |
| RAG | C | Runs well | 19.9 tok/s | 17648 ms | 237K |
How HelpingAI 9B 200k i1 (9B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C44 |
Q3_K_S | 3 | 4.4 GB | Low | C44 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI 9B 200k i1 on your machine.
Run
lms load hf-mradermacher--helpingai-9b-200k-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 243%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 120%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 243%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M3 Pro 36GB can run HelpingAI 9B 200k i1 with a C grade (Runs well). Expected decode speed: 19.9 tok/s.
HelpingAI 9B 200k i1 (9B parameters) requires approximately 11.3 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 9B 200k i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, HelpingAI 9B 200k i1 achieves approximately 19.9 tokens per second decode speed with a time-to-first-token of 9707ms using Q4_K_M quantization.
For coding workloads, HelpingAI 9B 200k i1 on MacBook Pro M3 Pro 36GB receives a C grade with 19.9 tok/s and 237K context.
On MacBook Pro M3 Pro 36GB, HelpingAI 9B 200k i1 can safely use up to 237K 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-mradermacher--helpingai-9b-200k-i1-gguf-on-m3-pro-36gb" 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 |
| C44 |
Q4_K_M | 4 | 5.5 GB | Medium | C45 |
Q5_K_M | 5 | 6.5 GB | High | C45 |
Q6_K | 6 | 7.4 GB | High | C46 |
Q8_0 | 8 | 9.6 GB | Very High | C47 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C49 |
Not always. MacBook Pro M3 Pro 36GB 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.