HelpingAI 9B 200k i1 needs ~14.4 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~35 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
35.2 tok/s
TTFT
5496 ms
Safe context
497K
Memory
14.4 GB / 46.1 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 | 35.2 tok/s | 2998 ms | 497K |
| Coding | C | Runs well | 35.2 tok/s | 5496 ms | 497K |
| Agentic Coding | C | Runs well | 35.2 tok/s | 7994 ms | 497K |
| Reasoning | C | Runs well | 35.2 tok/s | 6495 ms | 497K |
| RAG | C | Runs well | 35.2 tok/s | 9992 ms | 497K |
How HelpingAI 9B 200k i1 (9B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C41 |
Q3_K_S | 3 | 4.4 GB | Low | C41 |
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 94%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 188%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 140%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M4 Pro 64GB can run HelpingAI 9B 200k i1 with a C grade (Runs well). Expected decode speed: 35.2 tok/s.
HelpingAI 9B 200k i1 (9B parameters) requires approximately 14.4 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 M4 Pro 64GB, HelpingAI 9B 200k i1 achieves approximately 35.2 tokens per second decode speed with a time-to-first-token of 5496ms using Q4_K_M quantization.
For coding workloads, HelpingAI 9B 200k i1 on MacBook Pro M4 Pro 64GB receives a C grade with 35.2 tok/s and 497K context.
On MacBook Pro M4 Pro 64GB, HelpingAI 9B 200k i1 can safely use up to 497K 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-m4-pro-64gb" 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 |
| C41 |
Q4_K_M | 4 | 5.5 GB | Medium | C41 |
Q5_K_M | 5 | 6.5 GB | High | C42 |
Q6_K | 6 | 7.4 GB | High | C42 |
Q8_0 | 8 | 9.6 GB | Very High | C42 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C45 |
Not always. MacBook Pro M4 Pro 64GB 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.