Will It Run AI

Can Gemma 2 9B run on RTX 2000 Ada 16GB?

YES — Runs Great

B68Good
Estimated from fit model

Gemma 2 9B needs ~13.1 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~32 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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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) — 13.1 GB, 31.7 tok/s, Runs well
13.1 GB required16.0 GB available
82% VRAM used

Fit status

Runs well

Decode

31.7 tok/s

TTFT

6102 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 RTX 2000 Ada 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: 31.7 tok/s decode · 6.1s TTFT (warm) · 79 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well31.7 tok/s3329 ms8K
CodingBRuns well31.7 tok/s6102 ms8K
Agentic CodingCVery compromised (needs ~0.7 GB host RAM)18.1 tok/s15601 ms8K
ReasoningBRuns well31.7 tok/s7212 ms8K
RAGCVery compromised (needs ~0.7 GB host RAM)18.1 tok/s19501 ms8K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowB62
Q3_K_S
3
4.4 GB
LowB63
NVFP4
4
5.0 GB
MediumB63
Q4_K_M
4
5.5 GB
MediumB64
Q5_K_M
5
6.5 GB
HighB65
Q6_K
6
7.4 GB
HighB66
Q8_0Best for your GPU
8
9.6 GB
Very HighB66
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 4000 Ada 20GBOpción económica
20 GB VRAM (+4)360 GB/s (+72)
B
Sube la velocidad estimada de decodificación alrededor de un 28%.40.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 28%.

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

~$1,250 MSRP

NVIDIARTX 3090 24GBMejor relación calidad-precio
24 GB VRAM (+8)936 GB/s (+648)
B
Sube la velocidad estimada de decodificación alrededor de un 122%.70.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 122%.

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

~$1,499 MSRP

NVIDIARTX PRO 4000 Blackwell 24GBMejora NVIDIA
24 GB VRAM (+8)672 GB/s (+384)
B
Sube la velocidad estimada de decodificación alrededor de un 158%.81.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 158%.

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

~$1,599 MSRP

Frequently asked questions

Can RTX 2000 Ada 16GB run Gemma 2 9B?

Yes, RTX 2000 Ada 16GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 31.7 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 RTX 2000 Ada 16GB?

On RTX 2000 Ada 16GB, Gemma 2 9B achieves approximately 31.7 tokens per second decode speed with a time-to-first-token of 6102ms using Q4_K_M quantization.

Can RTX 2000 Ada 16GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RTX 2000 Ada 16GB receives a B grade with 31.7 tok/s and 8K context.

What context window can Gemma 2 9B use on RTX 2000 Ada 16GB?

On RTX 2000 Ada 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.

See all results for RTX 2000 Ada 16GBSee all hardware for Gemma 2 9B
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