RTX 5060 Ti 16GBBudget pick448 GB/s (+298)
C
Raises estimated decode speed by about 154%.50.6 tok/s decode
Raises estimated decode speed by about 154%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
HelpingAI 9B 200k i1 needs ~9.4 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 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
70K
Memory
9.4 GB / 13.0 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 | 70K |
| Coding | C | Runs well | 19.9 tok/s | 9707 ms | 70K |
| Agentic Coding | C | Runs well | 19.9 tok/s | 14119 ms | 70K |
| Reasoning | C | Runs well | 19.9 tok/s | 11471 ms | 70K |
| RAG | C | Runs well | 19.9 tok/s | 17648 ms | 70K |
How HelpingAI 9B 200k i1 (9B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C49 |
Q3_K_S | 3 | 4.4 GB | Low | C50 |
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
RTX 5060 Ti 16GBBudget pickRaises estimated decode speed by about 154%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Raises estimated decode speed by about 263%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Yes, MacBook Pro M3 Pro 18GB 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 9.4 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 18GB, 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 18GB receives a C grade with 19.9 tok/s and 70K context.
On MacBook Pro M3 Pro 18GB, HelpingAI 9B 200k i1 can safely use up to 70K 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-18gb" 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 |
| C51 |
Q4_K_M | 4 | 5.5 GB | Medium | C51 |
Q5_K_M | 5 | 6.5 GB | High | C52 |
Q6_K | 6 | 7.4 GB | High | C51 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | C51 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Not always. MacBook Pro M3 Pro 18GB 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.