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Gemma 3 27B Instruct

Gemma 3 27B Instruct needs roughly 20.1 GB VRAM at Q4_K_M quantization (62.2 GB at FP16). 75 GPUs we track can run it fully in VRAM at 8k context.

75 GPUs run this natively · 19 with CPU offload

Google27B params128k contextGemmaCommercial use ok

Gemma 3 27B Instruct is a 27B parameter dense model developed by Google. March 2025 multimodal model with native vision via SigLIP 400M encoder. 128K context with 5:1 local/global attention interleaving.

To run Gemma 3 27B Instruct locally: Q4_K_M needs ~32-33GB with context — 24GB GPU can run it but 32GB+ recommended for vision tasks. KV-cache optimization gives 60% memory reduction.

MMLU-Pro 67.5%, LiveCodeBench 29.7%, MMMU (vision) 64.9% — Chatbot Arena Elo 1338 ranks it best open non-thinking model.

VRAM at each quantization

Numbers here are computed at 8k context. Because KV cache grows linearly with context length, expect higher totals at longer sequence lengths.

QuantWeightsKV cacheTotal
FP32108.0 GB1.54 GB122.7 GB
BF1654.0 GB1.54 GB62.2 GB
FP1654.0 GB1.54 GB62.2 GB
Q8_028.7 GB1.54 GB33.9 GB
Q6_K22.2 GB1.54 GB26.6 GB
Q5_K_M19.2 GB1.54 GB23.3 GB
Q4_K_Mrec16.4 GB1.54 GB20.1 GB
Q3_K_M13.0 GB1.54 GB16.3 GB
Q2_K10.3 GB1.54 GB13.3 GB
NVFP4cuda13.5 GB1.54 GB16.9 GB

Shown at 8k context with FP16 KV cache. NVFP4 needs a CUDA GPU to run. Toggle TurboQuant in the calculator to view compressed KV cache numbers.

Benchmarks

MMLU-Pro
67.5

GPUs that run Gemma 3 27B Instruct natively (75)

Plus 19 GPUs that run it with CPU offload (slower)

Notes

5:1 local/global attention interleaving.

Hugging Face ↗Ollama ↗Released 2025-03-12

Compare Gemma 3 27B Instruct with other models

How to run Gemma 3 27B Instruct locally

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Q4_K_M needs 20.1 GB — fits a single high-end consumer GPU (24 GB).

Ollama

ollama run gemma3:27b

llama.cpp

./llama-cli -m gemma-3-27b-it.Q4_K_M.gguf -c 8192 -ngl 99

LM Studio: Search for 'Gemma 3 27B' in LM Studio. The Q4_K_M variant fits on a single 24 GB GPU. This model supports both text and image inputs.

Why this quantization? At 27B dense parameters, Q4_K_M keeps the model within reach of a single 24 GB GPU (roughly 17 GB for weights plus room for KV cache). Gemma 3 27B uses a 5:1 local/global attention interleaving pattern that keeps the KV cache smaller than a standard full-attention model at the same size. This architectural efficiency means Q4 preserves strong performance (MMLU-Pro: 67.5) while fitting on mainstream hardware.

Who is Gemma 3 27B Instruct for?

Users with a single 24 GB GPU who want a strong multimodal model that handles both text and images. Ideal for developers exploring vision-language tasks locally, or anyone who wants Google's latest open model running on their workstation.

Best for

  • Multimodal tasks combining text and image understanding
  • High-quality general chat and instruction following on a single 24 GB GPU
  • Content analysis that includes visual elements (charts, screenshots, documents)
  • Multilingual text generation leveraging Google's training data
  • Fine-tuning for domain-specific vision-language applications

Not ideal for

  • Users who only need text -- comparably sized text-only models like Qwen 2.5 32B may perform better on pure-text benchmarks
  • Workloads requiring very long context -- despite 128K support, practical context is limited by VRAM
  • Users who need Apache 2.0 licensing -- Gemma uses its own license

Continue reading

tutorials10 min
How to Run LLMs Locally on Mac (M1–M4 Guide)
hardware10 min
NVIDIA RTX 40 Series for LLMs: Complete VRAM & Performance Guide

Frequently asked questions

What are the VRAM requirements for Gemma 3 27B Instruct?
Gemma 3 27B Instruct requires approximately 20.1 GB of VRAM at Q4_K_M quantization, 33.9 GB at Q8, and 62.2 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 3 27B Instruct have?
Gemma 3 27B Instruct has 27 billion parameters.
How capable is Gemma 3 27B Instruct?
With an MMLU-Pro score of 67.5, Gemma 3 27B Instruct delivers solid general-purpose performance suitable for most everyday tasks and professional use.
Can Gemma 3 27B Instruct run on a 16 GB GPU?
No. At Q4_K_M, Gemma 3 27B Instruct needs 20.1 GB of VRAM — more than 16 GB. You will need a 24 GB GPU like the RTX 4090 or RTX 3090.
Can Gemma 3 27B Instruct run on a 24 GB GPU?
Yes. Gemma 3 27B Instruct fits in a 24 GB GPU at Q4_K_M, requiring 20.1 GB VRAM. GPUs with 24 GB include the RTX 4090, RTX 3090, and RTX 3090 Ti.
What is the smallest quantization for Gemma 3 27B Instruct that fits in 24 GB of VRAM?
At NVFP4, Gemma 3 27B Instruct needs 16.8 GB — the highest-quality quantization that fits in 24 GB of VRAM.
What GPU do I need to run Gemma 3 27B Instruct locally?
A 24 GB GPU is the minimum. At Q4_K_M, Gemma 3 27B Instruct needs 20.1 GB VRAM. Good options: RTX 4090 (24 GB), RTX 3090 (24 GB).