Intel Arc A770 16GBBest value560 GB/s (+410)
C
Raises estimated decode speed by about 129%.41.3 tok/s decode
Raises estimated decode speed by about 129%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
HelpingAI2.5 10B i1 needs ~10.1 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~18 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
18.0 tok/s
TTFT
10785 ms
Safe context
55K
Memory
10.1 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 | 18.0 tok/s | 5883 ms | 55K |
| Coding | C | Runs well | 18.0 tok/s | 10785 ms | 55K |
| Agentic Coding | C | Tight fit | 18.0 tok/s | 15687 ms | 55K |
| Reasoning | C | Runs well | 18.0 tok/s | 12746 ms | 55K |
| RAG | C | Tight fit | 18.0 tok/s | 19609 ms | 55K |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C49 |
Q3_K_S | 3 | 4.9 GB | Low | C50 |
NVFP4 | 4 | 5.6 GB | Medium | C51 |
Q4_K_M | 4 | 6.1 GB | Medium | C52 |
Q5_K_M | 5 | 7.2 GB | High | C51 |
Q6_KBest for your GPU | 6 | 8.2 GB | High | C51 |
Q8_0 | 8 | 10.7 GB | Very High | F0 |
F16 | 16 | 20.5 GB | Maximum | F0 |
Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server startUpgrade options
Intel Arc A770 16GBBest valueRaises estimated decode speed by about 129%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
Raises estimated decode speed by about 76%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yes, MacBook Pro M3 Pro 18GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 18.0 tok/s.
HelpingAI2.5 10B i1 (10B parameters) requires approximately 10.1 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 18GB, HelpingAI2.5 10B i1 achieves approximately 18.0 tokens per second decode speed with a time-to-first-token of 10785ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on MacBook Pro M3 Pro 18GB receives a C grade with 18.0 tok/s and 55K context.
On MacBook Pro M3 Pro 18GB, HelpingAI2.5 10B i1 can safely use up to 55K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai2-5-10b-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: