MacBook Air M4 24GBBudget pick
24 GB Unified (+8)
C
Adds memory headroom for longer context windows and future model growth.13 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
HelpingAI2.5 10B i1 needs ~9.9 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~13 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
Tight fit
Decode
13.0 tok/s
TTFT
14857 ms
Safe context
38K
Memory
9.9 GB / 11.5 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 | 13.0 tok/s | 8104 ms | 38K |
| Coding | C | Tight fit | 13.0 tok/s | 14857 ms | 38K |
| Agentic Coding | C | Runs with offload | 13.0 tok/s | 21610 ms | 38K |
| Reasoning | C | Tight fit | 13.0 tok/s | 17558 ms | 38K |
| RAG | C | Runs with offload | 13.0 tok/s | 27013 ms | 38K |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C50 |
Q3_K_S | 3 | 4.9 GB | Low | C52 |
NVFP4 | 4 | 5.6 GB | Medium | C52 |
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
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
RTX 3080 Ti 12GBBiggest leapRaises estimated decode speed by about 615%.
~$1,199 MSRP
Yes, MacBook Pro M4 16GB can run HelpingAI2.5 10B i1 with a C grade (Tight fit). Expected decode speed: 13.0 tok/s.
HelpingAI2.5 10B i1 (10B parameters) requires approximately 9.9 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 M4 16GB, HelpingAI2.5 10B i1 achieves approximately 13.0 tokens per second decode speed with a time-to-first-token of 14857ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on MacBook Pro M4 16GB receives a C grade with 13.0 tok/s and 38K context.
On MacBook Pro M4 16GB, HelpingAI2.5 10B i1 can safely use up to 38K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M4 16GB 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-m4-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: