Gemma 2 9B needs ~13.0 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~38 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
1.0 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.4 GB host RAM)
Decode
38.1 tok/s
TTFT
5079 ms
Safe context
8K
Memory
13.0 GB / 12.0 GB
Offload
10%
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 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 60.3 tok/s | 1751 ms | 8K |
| Coding | B | Very compromised (needs ~0.4 GB host RAM) | 38.1 tok/s | 5079 ms | 8K |
| Agentic Coding | F | Too heavy | 18.9 tok/s | 14863 ms | 8K |
| Reasoning | B | Very compromised (needs ~0.4 GB host RAM) | 38.1 tok/s | 6003 ms | 8K |
| RAG | F | Too heavy | 18.9 tok/s | 18578 ms |
How Gemma 2 9B (9B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B64 |
Q3_K_S | 3 | 4.4 GB | Low | B66 |
NVFP4 | 4 |
Copy-paste commands to run Gemma 2 9B on your machine.
Run
ollama run gemma2Upgrade options
RTX 5060 Ti 16GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 39%.
~$449 MSRP
RTX 4060 Ti 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
RTX 5070 Ti 16GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 188%.
~$749 MSRP
Yes, RTX 4000 Ada Laptop 12GB can run Gemma 2 9B with a B grade (Very compromised (needs ~0.4 GB host RAM)). Expected decode speed: 38.1 tok/s.
Gemma 2 9B (9B parameters) requires approximately 13.0 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 4000 Ada Laptop 12GB, Gemma 2 9B achieves approximately 38.1 tokens per second decode speed with a time-to-first-token of 5079ms using Q4_K_M quantization.
For coding workloads, Gemma 2 9B on RTX 4000 Ada Laptop 12GB receives a B grade with 38.1 tok/s and 8K context.
On RTX 4000 Ada Laptop 12GB, Gemma 2 9B can safely use up to 8K 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-2-9b-on-rtx-4000-ada-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 8K |
5.0 GB |
| Medium |
| B66 |
Q4_K_M | 4 | 5.5 GB | Medium | B67 |
Q5_K_M | 5 | 6.5 GB | High | B67 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | B66 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
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.