Gemma 2 27B needs ~28.1 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q3_K_S quantization, expect ~8 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
7.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.3 tok/s
TTFT
36316 ms
Safe context
6K
Memory
31.3 GB / 24.0 GB
Offload
20%
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.
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 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~1.1 GB host RAM) | 8.1 tok/s | 13065 ms | 6K |
| Coding | F | Too heavy | 5.3 tok/s | 36316 ms | 6K |
| Agentic Coding | F | Too heavy | 2.8 tok/s | 100721 ms | 6K |
| Reasoning | F | Too heavy | 5.3 tok/s | 42919 ms | 6K |
| RAG | F | Too heavy | 2.8 tok/s | 125901 ms | 6K |
How Gemma 2 27B (27B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | B69 |
Q3_K_S | 3 | 13.2 GB | Low | B70 |
NVFP4 | 4 | 15.1 GB | Medium | B69 |
Q4_K_MBest for your GPU | 4 | 16.5 GB | Medium | B69 |
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 |
Copy-paste commands to run Gemma 2 27B on your machine.
Run
ollama run gemma2:27bOpciones 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
Yes, NVIDIA L4 24GB can run Gemma 2 27B at Q3_K_S quantization (Very compromised (needs ~1.9 GB host RAM)). The recommended Q4_K_M requires 31.3 GB which exceeds available memory, but at Q3_K_S it needs only 28.1 GB. Expected decode speed: 7.8 tok/s.
Gemma 2 27B (27B parameters) requires approximately 31.3 GB at Q4_K_M quantization. On NVIDIA L4 24GB, it fits at Q3_K_S using 28.1 GB.
The recommended quantization is Q4_K_M, but on NVIDIA L4 24GB the best fitting quantization is Q3_K_S, which uses 28.1 GB.
On NVIDIA L4 24GB, Gemma 2 27B achieves approximately 7.8 tokens per second decode speed with a time-to-first-token of 24923ms using Q3_K_S quantization.
For coding workloads, Gemma 2 27B on NVIDIA L4 24GB receives a F grade with 5.3 tok/s and 6K context.
On NVIDIA L4 24GB, Gemma 2 27B can safely use up to 8K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.
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.
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