Gemma 3 27B vs Gemma 2 27B

The two models at a glance

About Gemma 3 27B

Google's flagship Gemma 3 at 27B — multimodal, 128K context, and an LMArena Elo of 1338 that beats Llama 3.1 405B at 15x smaller. Sets the bar for open chat under 30B. Strengths: LMArena Elo 1338 — beats Llama 3.1 405B at 15x smaller, Multimodal with vision input, 128K context window, 140 language coverage.

About Gemma 2 27B

The flagship of the Gemma 2 family from Google. Approaches 70B-class quality on a single 24GB GPU at Q4, with strong multilingual coverage. Strengths: Quality close to 70B-class models, Runs in roughly 16GB VRAM at Q4, Strong instruction following, Robust multilingual output.

How they compare

Gemma 3 27B comes from Google and Gemma 2 27B from Google. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.

Gemma 3 27B and Gemma 2 27B share the same 27B parameter class. Both need about 16 GB of VRAM at a Q4 quantization, so they fit the same GPU tier.

Where they overlap on benchmarks, Gemma 3 27B takes MMLU with 78.6 against 75.2 — a clear 3.4-point margin. For workloads weighted toward that benchmark, Gemma 3 27B is the stronger default.

For long-context work, Gemma 3 27B offers the bigger window (125k vs 8k tokens).

Memory, quantization & throughput

Across quantization levels, Gemma 3 27B requires Q4 ≈ 16 GB, Q5 ≈ 19 GB, Q8 ≈ 29 GB, FP16 ≈ 54 GB, while Gemma 2 27B requires Q4 ≈ 16 GB, Q5 ≈ 19 GB, Q8 ≈ 29 GB, FP16 ≈ 54 GB. In practice Gemma 3 27B needs a 16 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.

Without a GPU, Gemma 3 27B needs roughly 28 GB of system RAM to run on CPU and Gemma 2 27B about 28 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 13 tokens/sec from Gemma 3 27B and 13 from Gemma 2 27B, scaling up to 32 and 32 tokens/sec on high-end hardware.

Which fits your GPU

Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Gemma 3 27B or Gemma 2 27B to the card you actually own:

• On a 16 GB GPU: Gemma 3 27B runs at Q4 (16 GB); Gemma 2 27B runs at Q4 (16 GB).
• On a 24 GB GPU: Gemma 3 27B runs at Q5 (19 GB); Gemma 2 27B runs at Q5 (19 GB).

Benchmark scores

Reported benchmarks for Gemma 3 27B: LMArena Elo 73, MMLU 78.6, MMLU-Pro 67.5, MATH 89.

Reported benchmarks for Gemma 2 27B: MMLU 75.2, HellaSwag 89.5, HumanEval 51.8.

Bottom line: which should you pick?

• Pick Gemma 3 27B for long-context work (up to 125k tokens).
• Pick Gemma 3 27B if MMLU performance is your priority (78.6 vs 75.2).
• Pick Gemma 3 27B if your workload is multilingual, vision.

Which GPU should you buy to run Gemma 3 27B?

To run Gemma 3 27B locally at Q4, you need ~16 GB of VRAM. The best value for this is a RTX 5070 Ti (16 GB VRAM).

Frequently asked questions

What is the difference between Gemma 3 27B and Gemma 2 27B?

The headline differences: both are 27B models; their context windows differ (125k vs 8k tokens). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.

Can Gemma 3 27B and Gemma 2 27B run on a 24 GB GPU?

At a Q4 quantization, Gemma 3 27B needs about 16 GB of VRAM and fits comfortably on a 24 GB GPU; Gemma 2 27B needs about 16 GB and fits comfortably on a 24 GB GPU. Both have the same Q4 footprint.

Is Gemma 3 27B or Gemma 2 27B more capable?

On MMLU, Gemma 3 27B scores higher (78.6 vs 75.2), a 3.4-point advantage on this benchmark.

What licenses do Gemma 3 27B and Gemma 2 27B use?

Gemma 3 27B is licensed under Gemma and Gemma 2 27B under Gemma.

Which has the longer context window, Gemma 3 27B or Gemma 2 27B?

Gemma 3 27B has the larger context window (125k vs 8k tokens), so it handles longer documents and codebases in a single prompt.