Gemma 2 27B

When to pick this model

• Self-hosted assistants needing high quality on one consumer GPU
• Multilingual content workflows including French
• Instruction-heavy tasks like classification and structured output
• Workloads that don't require long context
• Replacing 70B models when VRAM is constrained

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, Gemma 2 27B needs a 16 GB card at Q4_K_M (16 GB). Stepping up to Q8_0 nearly doubles the footprint to 29 GB, and unquantized FP16 weights take 54 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, Gemma 2 27B needs roughly 28 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 3 tokens/sec on entry-level GPUs, on the order of 13 tokens/sec on a mid-range card, and up to 32 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches Gemma 2 27B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

Which hardware should you buy to run Gemma 2 27B?

To run Gemma 2 27B locally at Q4, you need ~16 GB for Q4 weights alone. Hardware option to compare: RTX 5070 Ti 16GB (GIGABYTE Gaming OC). Leave memory for the system and context; verify inference-engine support. A mini PC does not provide CUDA or macOS/MLX.

Published benchmark scores

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

To put Gemma 2 27B in context: its MMLU score of 75.2 ranks #14 of the 34 catalog models with a published MMLU result (catalog median 73.4); its HumanEval score of 51.8 ranks #23 of the 26 catalog models with a published HumanEval result (catalog median 81.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.

Strengths

• Quality close to 70B-class models
• Runs in roughly 16GB VRAM at Q4
• Strong instruction following
• Robust multilingual output

Limitations

• 8k context is a major handicap in 2025
• Gemma license is less permissive than Apache 2.0
• No vision in this checkpoint

Typical workloads

In our catalog grid, Gemma 2 27B is filed under Writing, Analysis — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: everyday chat, drafting and summarization.

Note the 8k-token context window — fine for short interactions, limiting for long documents or big retrieval contexts. It ships under the Gemma license — commercial use is generally possible but read the specific terms before embedding it in a product.

Architecture & training

Architecture: Dense Transformer · Gemma 2 27B · logit-softcapping

Training: 13T tokens. The largest in the Gemma 2 family.

Quick start

Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.

Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.

ollama run gemma2:27b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does Gemma 2 27B need?

At the recommended Q4_K_M quantization, Gemma 2 27B needs about 16 GB of VRAM. Q8_0 takes 29 GB, and unquantized FP16 weights take 54 GB.

Can Gemma 2 27B run without a GPU?

Yes — with roughly 28 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does Gemma 2 27B support?

Gemma 2 27B supports a 8k-token context window (8,192 tokens).

Can I use Gemma 2 27B commercially?

Gemma 2 27B ships under the Gemma license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.

How fast is Gemma 2 27B on consumer hardware?

Our compatibility engine estimates on the order of 13 tokens/sec on a mid-range GPU and up to 32 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of Gemma 2 27B should I download first?

Start with Q4_K_M (16 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.