CogVLM2 19B needs ~15.7 GB but RTX 2060 Super 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
7.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.0 tok/s
TTFT
48620 ms
Safe context
4K
Memory
15.7 GB / 8.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 15.7 GB, but this setup only exposes 8.0 GB of usable VRAM.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 4.8 tok/s | 22210 ms | 4K |
| Coding | F | Too heavy | 4.0 tok/s | 48620 ms | 4K |
| Agentic Coding | F | Too heavy | 3.6 tok/s | 77895 ms | 4K |
| Reasoning | F | Too heavy | 4.0 tok/s | 57460 ms | 4K |
| RAG | F | Too heavy | 3.6 tok/s | 97369 ms | 4K |
How CogVLM2 19B (19B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.4 GB | Low | F0 |
Q3_K_S | 3 | 9.3 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
RTX 5060 Ti 16GBBudget 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.
~$449 MSRP
RTX 4060 Ti 16GBBest 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.
~$499 MSRP
RTX 2000 Ada 16GBNVIDIA 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.
~$625 MSRP
RTX 5090 Laptop 24GBBiggest leapMakes 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.
No, CogVLM2 19B requires more memory than RTX 2060 Super 8GB provides.
CogVLM2 19B (19B parameters) requires approximately 15.7 GB of memory with Q4_K_M quantization.
The recommended quantization for CogVLM2 19B is Q4_K_M, which balances quality and memory efficiency.
On RTX 2060 Super 8GB, CogVLM2 19B achieves approximately 4.0 tokens per second decode speed with a time-to-first-token of 48620ms using Q4_K_M quantization.
For coding workloads, CogVLM2 19B on RTX 2060 Super 8GB receives a F grade with 4.0 tok/s and 4K context.
On RTX 2060 Super 8GB, CogVLM2 19B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/cogvlm2-19b-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:
| Medium |
| F0 |
Q4_K_M | 4 | 11.6 GB | Medium | F0 |
Q5_K_M | 5 | 13.7 GB | High | F0 |
Q6_K | 6 | 15.6 GB | High | F0 |
Q8_0 | 8 | 20.3 GB | Very High | F0 |
F16 | 16 | 38.9 GB | Maximum | F0 |
