RTX 5060 Ti 16GBBudget pick448 GB/s (+298)
C
Raises estimated decode speed by about 154%.91.1 tok/s decode
Raises estimated decode speed by about 154%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
HelpingAI2.5 5B i1 needs ~6.5 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~36 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
35.9 tok/s
TTFT
5393 ms
Safe context
193K
Memory
6.5 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 | 35.9 tok/s | 2941 ms | 193K |
| Coding | C | Runs well | 35.9 tok/s | 5393 ms | 193K |
| Agentic Coding | C | Runs well | 35.9 tok/s | 7844 ms | 193K |
| Reasoning | C | Runs well | 35.9 tok/s | 6373 ms | 193K |
| RAG | C | Runs well | 35.9 tok/s | 9805 ms | 193K |
How HelpingAI2.5 5B i1 (5B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.0 GB | Low | C47 |
Q3_K_S | 3 | 2.5 GB | Low | C48 |
NVFP4 | 4 |
Copy-paste commands to run HelpingAI2.5 5B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-5b-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
RTX 5070 Ti 16GBBest valueRaises estimated decode speed by about 165%.
Adds memory headroom for longer context windows and future model growth.
~$749 MSRP
Yes, MacBook Pro M3 Pro 18GB can run HelpingAI2.5 5B i1 with a C grade (Runs well). Expected decode speed: 35.9 tok/s.
HelpingAI2.5 5B i1 (5B parameters) requires approximately 6.5 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2.5 5B i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 18GB, HelpingAI2.5 5B i1 achieves approximately 35.9 tokens per second decode speed with a time-to-first-token of 5393ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 5B i1 on MacBook Pro M3 Pro 18GB receives a C grade with 35.9 tok/s and 193K context.
On MacBook Pro M3 Pro 18GB, HelpingAI2.5 5B i1 can safely use up to 193K 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--helpingai2-5-5b-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:
2.8 GB |
| Medium |
| C48 |
Q4_K_M | 4 | 3.1 GB | Medium | C48 |
Q5_K_M | 5 | 3.6 GB | High | C49 |
Q6_K | 6 | 4.1 GB | High | C49 |
Q8_0Best for your GPU | 8 | 5.4 GB | Very High | C51 |
F16 | 16 | 10.3 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.