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gemma
9B parameters
Commercial OK

Gemma 2 9B Instruct

Mid-size Gemma 2. Strong chat quality with a different training mix from Llama family.

License: Gemma Terms of Use·Released Jun 27, 2024·Context: 8,192 tokens
Our verdict
By Fredoline Eruo·Last verified May 6, 2026
7.6/10
Positioning

The Gemma 2 9B holds up as a "feels nice to talk to" small model. Distillation from Gemini gives it conversational warmth that newer models often lack. Better as a chat companion than as a workhorse.

Strengths
  • Warmest conversational tone among 7–12B models.
  • Strong multilingual — better than Llama 3.1 8B on European languages.
  • Stable runner support — every backend has well-tested Gemma 2 paths.
Limitations
  • Gemma license restrictiveness.
  • Math + reasoning lag Qwen 2.5 7B and Phi 3.5 Mini.
  • No multimodal — for that pick Gemma 3.
  • Superseded by Gemma 3 family in 2025.
Real-world performance on RTX 4090
  • Q4_K_M (5.4 GB): 90–105 tok/s decode, TTFT under 80 ms
  • Q5_K_M (6.4 GB): 80–94 tok/s
  • Q8_0 (9.6 GB): 60–74 tok/s
Should you run this locally?

Yes, for chat-focused use cases where conversational warmth matters more than benchmarks. No, for new deployments — pick Gemma 3 12B for an upgrade or Qwen 2.5 7B for raw capability.

How it compares
  • vs Llama 3.1 8B → Llama wins on instruction reliability; Gemma 2 9B wins on conversational warmth.
  • vs Qwen 2.5 7B → Qwen wins on raw capability; Gemma feels more pleasant to chat with.
  • vs Gemma 3 12B → Gemma 3 12B is the modern upgrade; only stick with Gemma 2 9B for legacy compatibility.
Run this yourself 
ollama pull gemma2:9b-instruct-q4_K_M
ollama run gemma2:9b-instruct-q4_K_M
Settings: Q4_K_M GGUF, 8192 ctx, llama.cpp/CUDA, RTX 4090
›Why this rating

7.6/10 — the previous-generation Gemma 9B. Still a fine 9B-class chat model with warmer conversational tone than Llama 3.1 8B, but no multimodal. Loses points to Gemma 3 12B and Qwen 2.5 7B.

Overview

Mid-size Gemma 2. Strong chat quality with a different training mix from Llama family.

Strengths

  • Strong chat
  • Different training mix from Llama

Weaknesses

  • Only 8K context
  • Superseded by Gemma 3/4

Quantization variants

Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.

QuantizationFile sizeVRAM required
Q4_K_M5.8 GB7 GB
Q8_09.8 GB12 GB

Get the model

Ollama

One-line install

ollama run gemma2:9bRead our Ollama review →

HuggingFace

Original weights

huggingface.co/google/gemma-2-9b-it

Source repository — direct quantization required.

Hardware that runs this

Cards with enough VRAM for at least one quantization of Gemma 2 9B Instruct.

NVIDIA GB200 NVL72
13824GB · nvidia
AMD Instinct MI355X
288GB · amd
AMD Instinct MI325X
256GB · amd
AMD Instinct MI300X
192GB · amd
NVIDIA B200
192GB · nvidia
NVIDIA H100 NVL
188GB · nvidia
NVIDIA H200
141GB · nvidia
Intel Gaudi 3
128GB · intel
Compare alternatives

Models worth comparing

Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.

Step up
More capable — bigger memory footprint
Step down
Smaller — faster, runs on weaker hardware

Frequently asked

What's the minimum VRAM to run Gemma 2 9B Instruct?

7GB of VRAM is enough to run Gemma 2 9B Instruct at the Q4_K_M quantization (file size 5.8 GB). Higher-quality quantizations need more.

Can I use Gemma 2 9B Instruct commercially?

Yes — Gemma 2 9B Instruct ships under the Gemma Terms of Use, which permits commercial use. Always read the license text before deployment.

What's the context length of Gemma 2 9B Instruct?

Gemma 2 9B Instruct supports a context window of 8,192 tokens (about 8K).

How do I install Gemma 2 9B Instruct with Ollama?

Run `ollama pull gemma2:9b` to download, then `ollama run gemma2:9b` to start a chat session. The default quantization is Q4_K_M.

Source: huggingface.co/google/gemma-2-9b-it

Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.