20 GB VRAM (+4)360 GB/s (+72)
B
Raises estimated decode speed by about 51%.40.7 tok/s decode
Raises estimated decode speed by about 51%.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Can Gemma 2 9B run on RTX 4060 Ti 16GB?
YES — Runs Great
B68Good
○Estimated from fit model
Gemma 2 9B needs ~13.1 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~29 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Share:
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 13.1 GB, 26.9 tok/s, Runs well
13.1 GB required16.0 GB available
82% VRAM used
Fit status
Runs well
Decode
26.9 tok/s
TTFT
7193 ms
Safe context
8K
Memory
13.1 GB / 16.0 GB
Memory breakdown
Weights5.5 GB
KV Cache5.1 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsGemma 2 9B on RTX 4060 Ti 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 26.9 tok/s decode · 7.2s TTFT (warm) · 67 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 26.9 tok/s | 3924 ms | 8K |
| Coding | B | Runs well | 29.0 tok/s | 6671 ms | 8K |
| Agentic Coding | C | Very compromised | 16.5 tok/s | 17054 ms | 8K |
| Reasoning | B | Runs well | 26.9 tok/s | 8501 ms | 8K |
| RAG | C | Very compromised (needs ~0.7 GB host RAM) | 15.3 tok/s | 22987 ms | 8K |
Quantization options
How Gemma 2 9B (9B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B62 |
Q3_K_S | 3 | 4.4 GB | Low | B63 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Gemma 2 9B on your machine.
Run
ollama run gemma2Upgrade options
Hardware that runs Gemma 2 9B well
24 GB VRAM (+8)936 GB/s (+648)
B
Raises estimated decode speed by about 162%.70.4 tok/s decode
Raises estimated decode speed by about 162%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
24 GB VRAM (+8)672 GB/s (+384)
B
Raises estimated decode speed by about 204%.81.8 tok/s decode
Raises estimated decode speed by about 204%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP