Can Gemma 2 9B run on RTX 2060 Super 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Gemma 2 9B needs ~12.6 GB but RTX 2060 Super 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.6 GB, exceeds 8.0 GB available

12.6 GB required8.0 GB available

158% VRAM needed

4.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.3 tok/s

TTFT

14525 ms

Safe context

Memory

12.6 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights5.5 GB

KV Cache5.1 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 2 9B on RTX 2060 Super 8GB
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: 13.3 tok/s decode · 14.5s TTFT (warm) · 33 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 12.6 GB, but this setup only exposes 8.0 GB of usable VRAM.

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

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	21.9 tok/s	4811 ms	4K
Coding	F	Too heavy	13.3 tok/s	14525 ms	4K
Agentic Coding	F	Too heavy	7.5 tok/s	37776 ms	4K
Reasoning	F	Too heavy	13.3 tok/s	17166 ms	4K
RAG	F	Too heavy	7.5 tok/s	47220 ms	4K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B68
Q3_K_S	3	4.4 GB	Low	B68
NVFP4Best for your GPU

Upgrade options

Hardware that runs Gemma 2 9B well

RTX 3060 12GBBest value

12 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.28.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 116%.

~$329 MSRP

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.53.1 tok/s decode

Makes 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.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.40.2 tok/s decode

Makes 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.

~$499 MSRP

Frequently asked questions

Can RTX 2060 Super 8GB run Gemma 2 9B?

No, Gemma 2 9B requires more memory than RTX 2060 Super 8GB provides.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 12.6 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 RTX 2060 Super 8GB?

On RTX 2060 Super 8GB, Gemma 2 9B achieves approximately 13.3 tokens per second decode speed with a time-to-first-token of 14525ms using Q4_K_M quantization.

Can RTX 2060 Super 8GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RTX 2060 Super 8GB receives a F grade with 13.3 tok/s and 4K context.

What context window can Gemma 2 9B use on RTX 2060 Super 8GB?

On RTX 2060 Super 8GB, Gemma 2 9B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 2 9B feels slow on RTX 2060 Super 8GB?

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.

See all results for RTX 2060 Super 8GB See all hardware for Gemma 2 9B

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