Will It Run AI

Can Gemma 2 9B run on RTX 4080 Laptop 12GB?

BARELY — Tight on Memory

B56Good
Estimated from fit model

Gemma 2 9B needs ~13.0 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~41 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload
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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) — 13.0 GB, 40.7 tok/s, Very compromised (needs ~0.4 GB host RAM)
13.0 GB required12.0 GB available
108% VRAM needed

1.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.4 GB host RAM)

Decode

40.7 tok/s

TTFT

4754 ms

Safe context

8K

Memory

13.0 GB / 12.0 GB

Offload

10%

Memory breakdown

Weights5.5 GB
KV Cache5.1 GB
Runtime1.2 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsGemma 2 9B on RTX 4080 Laptop 12GB
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: 40.7 tok/s decode · 4.8s TTFT (warm) · 102 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBTight fit64.4 tok/s1639 ms8K
CodingBVery compromised (needs ~0.4 GB host RAM)40.7 tok/s4754 ms8K
Agentic CodingFToo heavy20.2 tok/s13910 ms8K
ReasoningBVery compromised (needs ~0.4 GB host RAM)40.7 tok/s5618 ms8K
RAGFToo heavy20.2 tok/s17388 ms8K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on RTX 4080 Laptop 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowB64
Q3_K_S
3
4.4 GB
LowB66
NVFP4
4
5.0 GB
MediumB66
Q4_K_M
4
5.5 GB
MediumB67
Q5_K_M
5
6.5 GB
HighB67
Q6_KBest for your GPU
6
7.4 GB
HighB66
Q8_0
8
9.6 GB
Very HighF0
F16
16
18.5 GB
MaximumF0

Get started

Copy-paste commands to run Gemma 2 9B on your machine.

Run

ollama run gemma2

Opciones de mejora

Hardware que ejecuta bien Gemma 2 9B

NVIDIARTX 5060 Ti 16GBOpción económica
16 GB VRAM (+4)448 GB/s (+16)
B
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.53.1 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 30%.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+4)
B
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.40.2 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$499 MSRP

NVIDIARTX 5070 Ti 16GBMejora NVIDIA
16 GB VRAM (+4)896 GB/s (+464)
B
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.109.7 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 170%.

~$749 MSRP

Frequently asked questions

Can RTX 4080 Laptop 12GB run Gemma 2 9B?

Yes, RTX 4080 Laptop 12GB can run Gemma 2 9B with a B grade (Very compromised (needs ~0.4 GB host RAM)). Expected decode speed: 40.7 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 13.0 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 4080 Laptop 12GB?

On RTX 4080 Laptop 12GB, Gemma 2 9B achieves approximately 40.7 tokens per second decode speed with a time-to-first-token of 4754ms using Q4_K_M quantization.

Can RTX 4080 Laptop 12GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RTX 4080 Laptop 12GB receives a B grade with 40.7 tok/s and 8K context.

What context window can Gemma 2 9B use on RTX 4080 Laptop 12GB?

On RTX 4080 Laptop 12GB, 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.

What should I upgrade first if Gemma 2 9B feels slow on RTX 4080 Laptop 12GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 4080 Laptop 12GBSee all hardware for Gemma 2 9B
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