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Gemma 2 9B Instruct

Gemma 2 9B Instruct needs roughly 9.0 GB VRAM at Q4_K_M quantization (23.8 GB at FP16). 99 GPUs we track can run it fully in VRAM at 8k context.

99 GPUs run this natively · 5 with CPU offload

Google9.2B params8k contextGemmaCommercial use ok

Gemma 2 9B Instruct is a 9.2B parameter dense model developed by Google. June 2024 9B model with knowledge distillation from 27B teacher — best performance for its size class.

To run Gemma 2 9B Instruct locally: Q5_K_M ~6-7GB — runs on 8GB GPUs. Excellent quality-per-VRAM ratio.

MMLU-Pro 32.0%, competitive with models 2-3× larger. Trained 50× beyond compute-optimal.

VRAM at each quantization

Assumes 8k context. KV cache grows linearly with context length.

QuantWeightsKV cacheTotal
FP3236.8 GB2.82 GB44.4 GB
BF1618.4 GB2.82 GB23.8 GB
FP1618.4 GB2.82 GB23.8 GB
Q8_09.2 GB2.82 GB13.5 GB
Q6_K7.5 GB2.82 GB11.6 GB
Q5_K_Mrec5.9 GB2.82 GB9.8 GB
Q4_K_M5.2 GB2.82 GB9.0 GB
Q3_K_M4.0 GB2.82 GB7.6 GB
Q2_K3.0 GB2.82 GB6.5 GB
NVFP4cuda4.6 GB2.82 GB8.3 GB

KV cache shown at 8k context (FP16). NVFP4 requires a CUDA GPU. Enable TurboQuant in the calculator to see reduced KV cache estimates.

Benchmarks

MMLU-Pro
32
GPQA Diamond
31.5
IFEval
74.4
MATH
44.3
Arena ELO
1190

GPUs that run Gemma 2 9B Instruct natively (99)

Plus 5 GPUs that run it with CPU offload (slower)
Hugging Face ↗Ollama ↗Released 2024-06-27

Compare Gemma 2 9B Instruct with other models

Frequently asked questions

What are the VRAM requirements for Gemma 2 9B Instruct?
Gemma 2 9B Instruct requires approximately 9.0 GB of VRAM at Q4_K_M quantization, 13.5 GB at Q8, and 23.8 GB at FP16. These numbers assume 8k context window; VRAM scales linearly with context length due to the KV cache.
How many parameters does Gemma 2 9B Instruct have?
Gemma 2 9B Instruct has 9.2 billion parameters.
How capable is Gemma 2 9B Instruct?
Gemma 2 9B Instruct has an MMLU-Pro score of 32, making it well-suited for lightweight tasks, prototyping, and resource-constrained environments.
Can Gemma 2 9B Instruct run on a 16 GB GPU?
Yes. Gemma 2 9B Instruct needs 9.0 GB at Q4_K_M, which fits in a 16 GB GPU like the RTX 4080 or RTX 4070 Ti Super.
What is the smallest quantization for Gemma 2 9B Instruct that fits in 24 GB of VRAM?
At BF16, Gemma 2 9B Instruct needs 23.8 GB — the highest-quality quantization that fits in 24 GB of VRAM.
What GPU do I need to run Gemma 2 9B Instruct locally?
A 16 GB GPU is enough. At Q4_K_M, Gemma 2 9B Instruct needs 9.0 GB VRAM. Good options: RTX 4080 (16 GB), RTX 4070 Ti Super (16 GB).