Gemma 2 27B needs ~30.5 GB but RTX 4090 Laptop 16GB only has 16.0 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
14.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.7 tok/s
TTFT
34169 ms
Safe context
4K
Memory
30.5 GB / 16.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 30.5 GB, but this setup only exposes 16.0 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 | 8.7 tok/s | 12144 ms | 4K |
| Coding | F | Too heavy | 5.7 tok/s | 34169 ms | 4K |
| Agentic Coding | F | Too heavy | 4.4 tok/s | 63905 ms | 4K |
| Reasoning | F | Too heavy | 5.7 tok/s | 40381 ms | 4K |
| RAG | F | Too heavy | 4.4 tok/s | 79881 ms | 4K |
How Gemma 2 27B (27B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 10.5 GB | Low | A70 |
Q3_K_S | 3 | 13.2 GB | Low | F0 |
Upgrade options
RTX 5090 32GBBudget 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.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest 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.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA 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.
~$4,000 MSRP
No, Gemma 2 27B requires more memory than RTX 4090 Laptop 16GB provides.
Gemma 2 27B (27B parameters) requires approximately 30.5 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 4090 Laptop 16GB, Gemma 2 27B achieves approximately 5.7 tokens per second decode speed with a time-to-first-token of 34169ms using Q4_K_M quantization.
For coding workloads, Gemma 2 27B on RTX 4090 Laptop 16GB receives a F grade with 5.7 tok/s and 4K context.
On RTX 4090 Laptop 16GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-27b-on-rtx-4090-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
15.1 GB |
| Medium |
| F0 |
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 |
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.
