InternVL2 8B needs ~6.9 GB VRAM. GTX 1660 Ti 6GB has 6.0 GB. With Q2_K quantization, expect ~25 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
2.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
11.4 tok/s
TTFT
16912 ms
Safe context
4K
Memory
8.6 GB / 6.0 GB
Offload
30%
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 | F | Too heavy | 14.9 tok/s | 7087 ms | 4K |
| Coding | F | Too heavy | 11.4 tok/s | 16912 ms | 4K |
| Agentic Coding | F | Too heavy | 7.3 tok/s | 38497 ms | 4K |
| Reasoning | F | Too heavy | 11.4 tok/s | 19987 ms | 4K |
| RAG | F | Too heavy | 7.3 tok/s | 48122 ms | 4K |
How InternVL2 8B (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 | S86 |
Q3_K_S | 3 | 3.9 GB | Low | F0 |
Copy-paste commands to run InternVL2 8B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "OpenGVLab/InternVL2-8B" \
--hf-file "InternVL2-8B-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
RTX 3050 8GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 74%.
~$249 MSRP
RTX 5060 8GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 230%.
~$299 MSRP
RTX 3060 12GBBudget 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.
~$329 MSRP
Yes, GTX 1660 Ti 6GB can run InternVL2 8B at Q2_K quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 8.6 GB which exceeds available memory, but at Q2_K it needs only 6.9 GB. Expected decode speed: 25.1 tok/s.
InternVL2 8B (8B parameters) requires approximately 8.6 GB at Q4_K_M quantization. On GTX 1660 Ti 6GB, it fits at Q2_K using 6.9 GB.
The recommended quantization is Q4_K_M, but on GTX 1660 Ti 6GB the best fitting quantization is Q2_K, which uses 6.9 GB.
On GTX 1660 Ti 6GB, InternVL2 8B achieves approximately 25.1 tokens per second decode speed with a time-to-first-token of 7710ms using Q2_K quantization.
For coding workloads, InternVL2 8B on GTX 1660 Ti 6GB receives a F grade with 11.4 tok/s and 4K context.
On GTX 1660 Ti 6GB, InternVL2 8B can safely use up to 8K tokens of context at Q2_K quantization. The model's official context limit is 8K, 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/internvl2-8b-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.