HelpingAI2.5 10B i1 needs ~10.1 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~76 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
75.5 tok/s
TTFT
2563 ms
Safe context
97K
Memory
10.1 GB / 16.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 75.5 tok/s | 1398 ms | 97K |
| Coding | C | Runs well | 75.5 tok/s | 2563 ms | 97K |
| Agentic Coding | B | Runs well | 75.5 tok/s | 3728 ms | 97K |
| Reasoning | C | Runs well | 75.5 tok/s | 3029 ms | 97K |
| RAG | B | Runs well | 75.5 tok/s | 4660 ms | 97K |
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | C47 |
Q3_K_S | 3 | 4.9 GB | Low | C48 |
NVFP4 | 4 | 5.6 GB | Medium | C49 |
Q4_K_M | 4 | 6.1 GB | Medium | C49 |
Q5_K_M | 5 | 7.2 GB | High | C50 |
Q6_K | 6 | 8.2 GB | High | C51 |
Q8_0Best for your GPU | 8 | 10.7 GB | Very High | C50 |
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 startYes, RTX 4090 Laptop 16GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 75.5 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 RTX 4090 Laptop 16GB, HelpingAI2.5 10B i1 achieves approximately 75.5 tokens per second decode speed with a time-to-first-token of 2563ms using Q4_K_M quantization.
For coding workloads, HelpingAI2.5 10B i1 on RTX 4090 Laptop 16GB receives a C grade with 75.5 tok/s and 97K context.
On RTX 4090 Laptop 16GB, HelpingAI2.5 10B i1 can safely use up to 97K 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--helpingai2-5-10b-i1-gguf-on-rtx-4090-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: