Will It Run AI

Can Gemma 2 9B run on RTX 5060 8GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

Gemma 2 9B needs ~12.6 GB but RTX 5060 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

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

15.8 tok/s

TTFT

12270 ms

Safe context

4K

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 5060 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: 15.8 tok/s decode · 12.3s TTFT (warm) · 39 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.

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

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy25.1 tok/s4211 ms4K
CodingFToo heavy15.0 tok/s12883 ms4K
Agentic CodingFToo heavy7.9 tok/s35782 ms4K
ReasoningFToo heavy15.8 tok/s14500 ms4K
RAGFToo heavy7.9 tok/s44728 ms4K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on RTX 5060 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowB68
Q3_K_S
3
4.4 GB
LowB68
NVFP4Best for your GPU
4
5.0 GB
MediumB67
Q4_K_M
4
5.5 GB
MediumF0
Q5_K_M
5
6.5 GB
HighF0
Q6_K
6
7.4 GB
HighF0
Q8_0
8
9.6 GB
Very HighF0
F16
16
18.5 GB
MaximumF0

アップグレードオプション

Gemma 2 9Bを快適に動かすハードウェア

NVIDIARTX 3060 12GBコスパ最良
12 GB VRAM (+4)
C
Makes the model fit on the accelerator instead of staying completely out of reach.28.7 tok/sデコード

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

Raises estimated decode speed by about 82%.

〜$329 MSRP

NVIDIARTX 5060 Ti 16GBコスパ重視
16 GB VRAM (+8)
B
Makes the model fit on the accelerator instead of staying completely out of reach.53.1 tok/sデコード

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

NVIDIARTX 4060 Ti 16GBNVIDIAアップグレード
16 GB VRAM (+8)
B
Makes the model fit on the accelerator instead of staying completely out of reach.40.2 tok/sデコード

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 5060 8GB run Gemma 2 9B?

No, Gemma 2 9B requires more memory than RTX 5060 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 5060 8GB?

On RTX 5060 8GB, Gemma 2 9B achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12883ms using Q4_K_M quantization.

Can RTX 5060 8GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RTX 5060 8GB receives a F grade with 15.0 tok/s and 4K context.

What context window can Gemma 2 9B use on RTX 5060 8GB?

On RTX 5060 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 5060 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 5060 8GBSee all hardware for Gemma 2 9B
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