Gemma 2 9B needs ~12.6 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
4.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
13.3 tok/s
TTFT
14525 ms
Safe context
4K
Memory
12.6 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 12.6 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 | 21.9 tok/s | 4811 ms | 4K |
| Coding | F | Too heavy | 13.3 tok/s | 14525 ms | 4K |
| Agentic Coding | F | Too heavy | 7.5 tok/s | 37776 ms | 4K |
| Reasoning | F | Too heavy | 13.3 tok/s | 17166 ms | 4K |
| RAG | F | Too heavy | 7.5 tok/s | 47220 ms | 4K |
How Gemma 2 9B (9B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B68 |
Q3_K_S | 3 | 4.4 GB | Low | B68 |
NVFP4Best for your GPU | 4 | 5.0 GB | Medium | B67 |
Q4_K_M | 4 | 5.5 GB | Medium | F0 |
Q5_K_M | 5 | 6.5 GB | High | F0 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Opciones de mejora
RTX 3060 12GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 116%.
~$329 MSRP
RTX 5060 Ti 16GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$449 MSRP
RTX 4060 Ti 16GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$499 MSRP
No, Gemma 2 9B requires more memory than RTX 2060 Super 8GB provides.
Gemma 2 9B (9B parameters) requires approximately 12.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.
On RTX 2060 Super 8GB, Gemma 2 9B achieves approximately 13.3 tokens per second decode speed with a time-to-first-token of 14525ms using Q4_K_M quantization.
For coding workloads, Gemma 2 9B on RTX 2060 Super 8GB receives a F grade with 13.3 tok/s and 4K context.
On RTX 2060 Super 8GB, Gemma 2 9B 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/gemma-2-9b-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>
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