Helply 10.2b chat i1 needs ~9.1 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 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
1.1 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.8 GB host RAM)
Decode
22.9 tok/s
TTFT
8469 ms
Safe context
4K
Memory
9.1 GB / 8.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Runs with offload (needs ~0.4 GB host RAM) | 26.5 tok/s | 3980 ms | 4K |
| Coding | D | Very compromised (needs ~0.8 GB host RAM) | 22.9 tok/s | 8469 ms | 4K |
| Agentic Coding | F | Too heavy | 17.4 tok/s | 16146 ms | 4K |
| Reasoning | D | Very compromised (needs ~0.8 GB host RAM) | 22.9 tok/s | 10009 ms | 4K |
| RAG | F | Too heavy | 17.4 tok/s | 20182 ms |
How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.0 GB | Low | C53 |
Q3_K_SBest for your GPU | 3 | 5.0 GB | Low | C52 |
Copy-paste commands to run Helply 10.2b chat i1 on your machine.
Run
lms load hf-mradermacher--helply-10-2b-chat-i1-gguf && lms server startUpgrade options
RTX 3060 12GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 63%.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 89%.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 55%.
~$499 MSRP
Yes, RTX 2060 Super 8GB can run Helply 10.2b chat i1 with a D grade (Very compromised (needs ~0.8 GB host RAM)). Expected decode speed: 22.9 tok/s.
Helply 10.2b chat i1 (10.199999809265137B parameters) requires approximately 9.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Helply 10.2b chat i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 2060 Super 8GB, Helply 10.2b chat i1 achieves approximately 22.9 tokens per second decode speed with a time-to-first-token of 8469ms using Q4_K_M quantization.
For coding workloads, Helply 10.2b chat i1 on RTX 2060 Super 8GB receives a D grade with 22.9 tok/s and 4K context.
On RTX 2060 Super 8GB, Helply 10.2b chat i1 can safely use up to 4K 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--helply-10-2b-chat-i1-gguf-on-rtx-2060-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
5.7 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 6.2 GB | Medium | F0 |
Q5_K_M | 5 | 7.3 GB | High | F0 |
Q6_K | 6 | 8.4 GB | High | F0 |
Q8_0 | 8 | 10.9 GB | Very High | F0 |
F16 | 16 | 20.9 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.