WizardLM 13B needs ~22.1 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
14.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.9 tok/s
TTFT
39390 ms
Safe context
4K
Memory
22.1 GB / 8.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 22.1 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 | 5.2 tok/s | 20338 ms | 4K |
| Coding | F | Too heavy | 4.9 tok/s | 39390 ms | 4K |
| Agentic Coding | F | Too heavy | 4.9 tok/s | 57294 ms | 4K |
| Reasoning | F | Too heavy | 4.9 tok/s | 46551 ms | 4K |
| RAG | F | Too heavy | 4.9 tok/s | 71617 ms | 4K |
How WizardLM 13B (13B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 5.1 GB | Low | A73 |
Q3_K_S | 3 | 6.4 GB | Low | F0 |
Upgrade options
RTX 4000 Ada 20GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 292%.
~$1,250 MSRP
RTX 3090 24GBBudget 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.
~$1,499 MSRP
RTX 4090 24GBNVIDIA 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.
~$1,599 MSRP
No, WizardLM 13B requires more memory than RTX 2060 Super 8GB provides.
WizardLM 13B (13B parameters) requires approximately 22.1 GB of memory with Q4_K_M quantization.
The recommended quantization for WizardLM 13B is Q4_K_M, which balances quality and memory efficiency.
On RTX 2060 Super 8GB, WizardLM 13B achieves approximately 4.9 tokens per second decode speed with a time-to-first-token of 39390ms using Q4_K_M quantization.
For coding workloads, WizardLM 13B on RTX 2060 Super 8GB receives a F grade with 4.9 tok/s and 4K context.
On RTX 2060 Super 8GB, WizardLM 13B can safely use up to 4K tokens of context. 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/wizardlm-13b-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:
| 4 |
7.3 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 7.9 GB | Medium | F0 |
Q5_K_M | 5 | 9.4 GB | High | F0 |
Q6_K | 6 | 10.7 GB | High | F0 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 GB | Maximum | F0 |
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.
