MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+4)410 GB/s (+210)
C
Raises estimated decode speed by about 116%.51.3 tok/s decode
Raises estimated decode speed by about 116%.
~$2,499 MSRP
HelpingAI 9B 200k i1 needs ~10.9 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~24 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
23.7 tok/s
TTFT
8176 ms
Safe context
200K
Memory
10.9 GB / 23.0 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 | 23.7 tok/s | 4460 ms | 200K |
| Coding | C | Runs well | 23.7 tok/s | 8176 ms | 200K |
| Agentic Coding | C | Runs well | 23.7 tok/s | 11892 ms | 200K |
| Reasoning | C | Runs well | 23.7 tok/s | 9662 ms | 200K |
| RAG | C | Runs well | 23.7 tok/s | 14865 ms | 200K |
How HelpingAI 9B 200k i1 (9B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C44 |
Q3_K_S | 3 | 4.4 GB | Low | C45 |
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 116%.
~$2,499 MSRP
Raises estimated decode speed by about 188%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 257%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M1 Pro 32GB can run HelpingAI 9B 200k i1 with a C grade (Runs well). Expected decode speed: 23.7 tok/s.
HelpingAI 9B 200k i1 (9B parameters) requires approximately 10.9 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 M1 Pro 32GB, HelpingAI 9B 200k i1 achieves approximately 23.7 tokens per second decode speed with a time-to-first-token of 8176ms using Q4_K_M quantization.
For coding workloads, HelpingAI 9B 200k i1 on MacBook Pro M1 Pro 32GB receives a C grade with 23.7 tok/s and 200K context.
On MacBook Pro M1 Pro 32GB, HelpingAI 9B 200k i1 can safely use up to 200K 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-m1-pro-32gb" 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 |
| C45 |
Q4_K_M | 4 | 5.5 GB | Medium | C46 |
Q5_K_M | 5 | 6.5 GB | High | C46 |
Q6_K | 6 | 7.4 GB | High | C47 |
Q8_0 | 8 | 9.6 GB | Very High | C48 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C49 |
Not always. MacBook Pro M1 Pro 32GB 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.