Gemma 2 27B needs ~30.9 GB but RTX 4000 Ada 20GB only has 20.0 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
10.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.4 tok/s
TTFT
36030 ms
Safe context
4K
Memory
30.9 GB / 20.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 30.9 GB, but this setup only exposes 20.0 GB of usable VRAM.
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 | 8.2 tok/s | 12885 ms | 4K |
| Coding | F | Too heavy | 5.4 tok/s | 36030 ms | 4K |
| Agentic Coding | F | Too heavy | 2.8 tok/s | 100646 ms | 4K |
| Reasoning | F | Too heavy | 5.4 tok/s | 42580 ms | 4K |
| RAG | F | Too heavy | 2.8 tok/s | 125807 ms | 4K |
How Gemma 2 27B (27B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | A70 |
Q3_K_S | 3 | 13.2 GB | Low | B70 |
NVFP4Best for your GPU | 4 | 15.1 GB | Medium | B69 |
Q4_K_M | 4 | 16.5 GB | Medium | F0 |
Q5_K_M | 5 | 19.4 GB | High | F0 |
Q6_K | 6 | 22.1 GB | High | F0 |
Q8_0 | 8 | 28.9 GB | Very High | F0 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Opciones de mejora
RTX 5090 32GBOpción 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.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBMejor relación calidad-precioHace 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.
~$2,499 MSRP
RTX 5000 Ada 32GBMejora 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.
~$4,000 MSRP
No, Gemma 2 27B requires more memory than RTX 4000 Ada 20GB provides.
Gemma 2 27B (27B parameters) requires approximately 30.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 27B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4000 Ada 20GB, Gemma 2 27B achieves approximately 5.4 tokens per second decode speed with a time-to-first-token of 36030ms using Q4_K_M quantization.
For coding workloads, Gemma 2 27B on RTX 4000 Ada 20GB receives a F grade with 5.4 tok/s and 4K context.
On RTX 4000 Ada 20GB, Gemma 2 27B 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-2-27b-on-rtx-4000-ada-20gb" 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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