RTX 4000 Ada 20GB经济之选20 GB VRAM (+4)360 GB/s (+40)
B
Raises estimated decode speed by about 35%.40.7 tok/s 解码
Raises estimated decode speed by about 35%.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Can Gemma 2 9B run on NVIDIA T4 16GB?
YES — Runs Great
B68Good
Estimated from fit model
Gemma 2 9B needs ~13.1 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~30 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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, 30.1 tok/s, Runs well
13.1 GB required16.0 GB available
82% VRAM used
Fit status
Runs well
Decode
30.1 tok/s
TTFT
6422 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 NVIDIA T4 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: 30.1 tok/s decode · 6.4s TTFT (warm) · 75 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 30.1 tok/s | 3503 ms | 8K |
| Coding | B | Runs well | 30.1 tok/s | 6422 ms | 8K |
| Agentic Coding | C | Very compromised (needs ~0.7 GB host RAM) | 16.5 tok/s | 17083 ms | 8K |
| Reasoning | B | Runs well | 30.1 tok/s | 7589 ms | 8K |
| RAG | C | Very compromised (needs ~0.7 GB host RAM) | 16.5 tok/s | 21353 ms | 8K |
Quantization options
How Gemma 2 9B (9B params) fits at each quantization level on NVIDIA T4 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 | 5.0 GB | Medium | B63 |
Q4_K_M | 4 | 5.5 GB | Medium | B64 |
Q5_K_M | 5 | 6.5 GB | High | B65 |
Q6_K | 6 | 7.4 GB | High | B66 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | B66 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Gemma 2 9B on your machine.
Run
ollama run gemma2升级选项
能流畅运行 Gemma 2 9B 的硬件
RTX 4000 Ada 20GB经济之选RTX 3090 24GB最佳性价比24 GB VRAM (+8)936 GB/s (+616)
B
Raises estimated decode speed by about 134%.70.4 tok/s 解码
Raises estimated decode speed by about 134%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIA 升级24 GB VRAM (+8)672 GB/s (+352)
B
Raises estimated decode speed by about 172%.81.8 tok/s 解码
Raises estimated decode speed by about 172%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Frequently asked questions
Can NVIDIA T4 16GB run Gemma 2 9B?
Yes, NVIDIA T4 16GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 30.1 tok/s.
How much VRAM does Gemma 2 9B need?
Gemma 2 9B (9B parameters) requires approximately 13.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Gemma 2 9B?
The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.
What speed will Gemma 2 9B run at on NVIDIA T4 16GB?
On NVIDIA T4 16GB, Gemma 2 9B achieves approximately 30.1 tokens per second decode speed with a time-to-first-token of 6422ms using Q4_K_M quantization.
Can NVIDIA T4 16GB run Gemma 2 9B for coding?
For coding workloads, Gemma 2 9B on NVIDIA T4 16GB receives a B grade with 30.1 tok/s and 8K context.
What context window can Gemma 2 9B use on NVIDIA T4 16GB?
On NVIDIA T4 16GB, 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-9b-on-t4-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: