HelpingAI 9B i1 needs ~17.8 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~42 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
42.3 tok/s
TTFT
4581 ms
Safe context
794K
Memory
17.8 GB / 69.1 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 | 42.3 tok/s | 2499 ms | 794K |
| Coding | C | Runs well | 42.3 tok/s | 4581 ms | 794K |
| Agentic Coding | C | Runs well | 42.3 tok/s | 6664 ms | 794K |
| Reasoning | C | Runs well | 42.3 tok/s | 5414 ms | 794K |
| RAG | C | Runs well | 42.3 tok/s | 8330 ms | 794K |
How HelpingAI 9B i1 (9B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | D40 |
Q3_K_S | 3 | 4.4 GB | Low | D40 |
NVFP4 | 4 | 5.0 GB | Medium | D40 |
Q4_K_M | 4 | 5.5 GB | Medium | D40 |
Q5_K_M | 5 | 6.5 GB | High | D40 |
Q6_K | 6 | 7.4 GB | High | D40 |
Q8_0 | 8 | 9.6 GB | Very High | C40 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C42 |
Copy-paste commands to run HelpingAI 9B i1 on your machine.
Run
lms load hf-mradermacher--helpingai-9b-i1-gguf && lms server startUpgrade-Optionen
Raises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
Raises estimated decode speed by about 89%.
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
Raises estimated decode speed by about 61%.
Adds memory headroom for longer context windows and future model growth.
ca. $4,999 MSRP
Yes, MacBook Pro M2 Max 96GB can run HelpingAI 9B i1 with a C grade (Runs well). Expected decode speed: 42.3 tok/s.
HelpingAI 9B i1 (9B parameters) requires approximately 17.8 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI 9B i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Max 96GB, HelpingAI 9B i1 achieves approximately 42.3 tokens per second decode speed with a time-to-first-token of 4581ms using Q4_K_M quantization.
For coding workloads, HelpingAI 9B i1 on MacBook Pro M2 Max 96GB receives a C grade with 42.3 tok/s and 794K context.
On MacBook Pro M2 Max 96GB, HelpingAI 9B i1 can safely use up to 794K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M2 Max 96GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai-9b-i1-gguf-on-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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