vntl llama3 8b v2 needs ~6.7 GB VRAM. GTX 1660 Ti 6GB has 6.0 GB. With Q3_K_S 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
1.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
14.0 tok/s
TTFT
13811 ms
Safe context
4K
Memory
7.6 GB / 6.0 GB
Offload
20%
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 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~0.8 GB host RAM) | 16.1 tok/s | 6553 ms | 4K |
| Coding | F | Too heavy | 14.0 tok/s | 13811 ms | 4K |
| Agentic Coding | F | Too heavy | 10.9 tok/s | 25921 ms | 4K |
| Reasoning | F | Too heavy | 14.0 tok/s | 16323 ms | 4K |
| RAG | F | Too heavy | 10.9 tok/s | 32402 ms | 4K |
How vntl llama3 8b v2 (8B params) fits at each quantization level on GTX 1660 Ti 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | F0 |
Copy-paste commands to run vntl llama3 8b v2 on your machine.
Run
lms load hf-lmg-anon--vntl-llama3-8b-v2-gguf && lms server startUpgrade options
RTX 3050 8GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$249 MSRP
RTX 5060 8GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$299 MSRP
RTX 4060 8GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$299 MSRP
Yes, GTX 1660 Ti 6GB can run vntl llama3 8b v2 at Q3_K_S quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 7.6 GB which exceeds available memory, but at Q3_K_S it needs only 6.7 GB. Expected decode speed: 21.8 tok/s.
vntl llama3 8b v2 (8B parameters) requires approximately 7.6 GB at Q4_K_M quantization. On GTX 1660 Ti 6GB, it fits at Q3_K_S using 6.7 GB.
The recommended quantization is Q4_K_M, but on GTX 1660 Ti 6GB the best fitting quantization is Q3_K_S, which uses 6.7 GB.
On GTX 1660 Ti 6GB, vntl llama3 8b v2 achieves approximately 21.8 tokens per second decode speed with a time-to-first-token of 8875ms using Q3_K_S quantization.
For coding workloads, vntl llama3 8b v2 on GTX 1660 Ti 6GB receives a F grade with 14.0 tok/s and 4K context.
On GTX 1660 Ti 6GB, vntl llama3 8b v2 can safely use up to 5K tokens of context at Q3_K_S quantization. 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-lmg-anon--vntl-llama3-8b-v2-gguf-on-gtx-1660-ti-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
4.5 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 4.9 GB | Medium | F0 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 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.