HelpingAI2.5 5B i1 needs ~7.1 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~22 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
22.3 tok/s
TTFT
8684 ms
Safe context
293K
Memory
7.1 GB / 17.3 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 | 22.3 tok/s | 4736 ms | 293K |
| Coding | C | Runs well | 22.3 tok/s | 8684 ms | 293K |
| Agentic Coding | C | Runs well | 22.3 tok/s | 12631 ms | 293K |
| Reasoning | C | Runs well | 22.3 tok/s | 10262 ms | 293K |
| RAG | C | Runs well | 22.3 tok/s | 15788 ms | 293K |
How HelpingAI2.5 5B i1 (5B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.0 GB | Low | C45 |
Q3_K_S | 3 | 2.5 GB | Low | C46 |
NVFP4 | 4 | 2.8 GB | Medium | C46 |
Q4_K_M | 4 | 3.1 GB | Medium | C46 |
Q5_K_M | 5 | 3.6 GB | High | C46 |
Q6_K | 6 | 4.1 GB | High | C47 |
Q8_0 | 8 | 5.4 GB | Very High | C48 |
F16Best for your GPU | 16 | 10.3 GB | Maximum | C50 |
Copy-paste commands to run HelpingAI2.5 5B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-5b-i1-gguf && lms server startOpciones de mejora
Sube la velocidad estimada de decodificación alrededor de un 214%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,999 MSRP
Sube la velocidad estimada de decodificación alrededor de un 106%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,999 MSRP
Sube la velocidad estimada de decodificación alrededor de un 91%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,999 MSRP
Yes, MacBook Pro M3 24GB can run HelpingAI2.5 5B i1 with a C grade (Runs well). Expected decode speed: 22.3 tok/s.
HelpingAI2.5 5B i1 (5B parameters) requires approximately 7.1 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 24GB, HelpingAI2.5 5B i1 achieves approximately 22.3 tokens per second decode speed with a time-to-first-token of 8684ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 5B i1 on MacBook Pro M3 24GB receives a C grade with 22.3 tok/s and 293K context.
On MacBook Pro M3 24GB, HelpingAI2.5 5B i1 can safely use up to 293K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M3 24GB 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--helpingai2-5-5b-i1-gguf-on-m3-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: