Can Gemma 2 9B run on RTX 4060 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Gemma 2 9B needs ~12.6 GB but RTX 4060 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

10.9 tok/s

TTFT

17735 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 4060 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: 10.9 tok/s decode · 17.7s TTFT (warm) · 27 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	17.6 tok/s	5997 ms	4K
Coding	F	Too heavy	10.9 tok/s	17735 ms	4K
Agentic Coding	F	Too heavy	5.7 tok/s	49435 ms	4K
Reasoning	F	Too heavy	10.9 tok/s	20960 ms	4K
RAG	F	Too heavy	5.7 tok/s	61793 ms	4K

Inference speed

Gemma 2 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 2 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	125.3	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	86.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	80.7	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	78.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	71.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	67.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	63.8	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	54.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	45.9	Fits
RTX 4070 12GB	12 GB	Q4_K_M	45.7	Heavy offload
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	42.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	37.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	28.7	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	10.9	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on RTX 4060 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	4	5.0 GB	Medium	B67
Q4_K_M	4	5.5 GB	Medium	F0
Q5_K_M	5	6.5 GB	High	F0
Q6_K	6	7.4 GB	High	F0
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Opções de upgrade

Hardware que roda bem Gemma 2 9B

RTX 3060 12GBMelhor custo-benefício

12 GB VRAM (+4)360 GB/s (+88)

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

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

Raises estimated decode speed by about 163%.

~$329 MSRP

RTX 5060 Ti 16GBOpção econômica

16 GB VRAM (+8)448 GB/s (+176)

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

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 16GBUpgrade NVIDIA

16 GB VRAM (+8)288 GB/s (+16)

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

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 4060 8GB run Gemma 2 9B?

No, Gemma 2 9B requires more memory than RTX 4060 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 4060 8GB?

On RTX 4060 8GB, Gemma 2 9B achieves approximately 10.9 tokens per second decode speed with a time-to-first-token of 17735ms using Q4_K_M quantization.

Can RTX 4060 8GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RTX 4060 8GB receives a F grade with 10.9 tok/s and 4K context.

What context window can Gemma 2 9B use on RTX 4060 8GB?

On RTX 4060 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 4060 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 4060 8GB See all hardware for Gemma 2 9B

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