v2.2 · 2026-06-12

VISION / MULTIMODAL MODEL — RUNTIME REQUIREMENTS

·approximateDownloadGb includes the Q4_K_M model plus a matching multimodal projection file where applicable.

·RAM and VRAM figures are estimates based on GGUF file size plus conservative runtime and KV-cache overhead, not official hardware requirements.

Gemma 3 27B Instruct

gemma-3-27b-it

MULTIMODAL

Gemma 3 27B Instruct is a 27B multimodal model profile for local AI planning. This page records provider, license, context, quantization, hardware-fit estimates, setup hints, source links, and caveats so readers do not choose a model by name alone.

PROVIDER

Google

FAMILY

Gemma 3

MODEL TYPE

multimodal

PARAMETERS

27B

MODALITIES

text, image, video

ARCHITECTURE

decoder-only Transformer vision-language instruction model

CONTEXT WINDOW

131,072 tokens

TRAINING TOKENS

14.0T

RELEASE DATE

2025-03-10

LICENSE & LINKS

Gemma Terms of Uselicense text

MODEL CARD

ai.google.dev/gemma/docs/core/model_card_3

HUGGING FACE

google/gemma-3-27b-it

GGUF REPOSITORIES

bartowski/google_gemma-3-27b-it-GGUF community conversion

Community llama.cpp GGUF quantization of the official Google instruction-tuned checkpoint; image input requires a compatible mmproj file in llama.cpp-style runners.

SETUP HINTS

OLLAMA

ollama run gemma3:27b

LM STUDIO

Search for bartowski/google_gemma-3-27b-it-GGUF and verify image/mmproj support before enabling vision.

LLAMA.CPP

Use hf.co/bartowski/google_gemma-3-27b-it-GGUF:Q4_K_M plus the matching mmproj file for image input.

QUANTIZATION FILE SIZES (GGUF)

Q4_K_M

16.55 GB

Q5_K_M

19.27 GB

Q8_0

28.71 GB

Quantization note: Q4_K_M for default local use; Q5_K_M if the model and multimodal projection fit comfortably in RAM/VRAM.

RAM / VRAM ESTIMATES

MIN RAM

32 GB

COMFORTABLE RAM

64 GB

MIN VRAM

24 GB

COMFORTABLE VRAM

32 GB

These are conservative local-inference estimates, not official hardware requirements.

HARDWARE FIT

CPU only (no GPU)

slow

8 GB RAM

16 GB RAM

32 GB RAM

borderline

8 GB VRAM

12 GB VRAM

24 GB VRAM

borderline

Apple Silicon (unified memory)

borderline

Hardware fit values are conservative local-inference estimates based on GGUF size plus runtime and KV-cache overhead. Actual requirements depend on context length, quantization, runtime, GPU offload, and other running apps.

BEST FOR

·highest-quality Gemma 3 local chat

·multimodal reasoning

·long-context work

·workstation inference

AVOID IF

·less than 32GB system RAM is available

·less than 24GB VRAM is available

·low latency is required

CAVEATS

·Video understanding is described by Google for Gemma 3 generally, but local GGUF/Ollama support should be treated as runner-dependent.

·GGUF files are community conversions, not official Google GGUF releases.