Guide · 2026-06-18

Gemma 2 27B vs Qwen 2.5 32B — Google vs Alibaba

Last updated 2026-06-18

Two mid-weight open-weights heavyweights, one 24 GB GPU slot. Benchmarks, throughput, license, and a clear verdict for 2026 local deployments.

By Mohamed Meguedmi · 10 min read

The 27B–32B mid-weight tier is where most serious local-LLM deployments live in 2026. The class fits a single 24 GB consumer GPU at 4-bit quantization, sustains 25–50 tokens/second on an RTX 4090-class card, and closes a large fraction of the quality gap to frontier closed-weight models. Two contenders dominate this slot: Google's Gemma 2 27B Instruct, released June 27, 2024, and Alibaba's Qwen 2.5 32B Instruct, released September 19, 2024. Both are open-weights, both target the same hardware envelope, and both still anchor most published comparisons against Llama 3.1 70B and Mixtral 8x22B. Here is how they stack up — and which one deserves the default slot on a local node.

Models at a glance

Despite landing in the same size class, the two models come from very different training pipelines. Gemma 2 distills knowledge from Google's larger Gemini family and introduces alternating local–global attention with a 4,096-token sliding window. Qwen 2.5 uses standard grouped-query attention but ships with a YaRN-extended 131K context and was trained on 18T tokens — 5T more than Gemma 2.

Spec	Gemma 2 27B IT	Qwen 2.5 32B Instruct
Parameters	27.2 B	32.5 B
Layers	46	64
Context window	8,192	32,768 native / 131,072 YaRN
Tokenizer vocab	256,128 (SentencePiece)	151,936 (BPE)
Training tokens	13 T	18 T
Attention	GQA + sliding window	GQA
Released	June 27, 2024	September 19, 2024
License	Gemma Terms of Use	Apache 2.0

Hardware footprint and quantization

The 5 B-parameter gap looks small on paper but it shifts where each model lands relative to common VRAM ceilings. At 4-bit, Gemma 2 27B fits a 20 GB card with room for a meaningful KV cache; Qwen 2.5 32B is a tighter fit on 24 GB once you load an 8K-context KV cache. At 8-bit you need at least 32 GB across one or two cards.

Quant level	Gemma 2 27B (GGUF)	Qwen 2.5 32B (GGUF)	Min. GPU
BF16	~54 GB	~65 GB	A100 80 GB / 2×4090
Q8_0	~29 GB	~35 GB	RTX 6000 Ada 48 GB
Q6_K	~22 GB	~27 GB	RTX 3090/4090 24 GB (tight)
Q5_K_M	~19 GB	~23 GB	RTX 3090/4090 24 GB
Q4_K_M	~17 GB	~20 GB	RTX 4080 16 GB (Gemma only)
Q3_K_M	~13 GB	~16 GB	RTX 4070 Ti 12 GB (Gemma only)

The headline implication: Gemma 2 27B is the only one of the two that comfortably runs on a 16 GB consumer card at usable quality. Below that, both models degrade quickly. Use the BestLLMfor cost calculator to translate these footprints into electricity and cloud-equivalent dollar figures for your region.

Benchmark scorecard

Numbers below are the official reported figures from the respective model cards and the Qwen 2.5 technical report. Where authors disagree on prompts, we used the Qwen team's harmonized comparison table.

Benchmark	Gemma 2 27B IT	Qwen 2.5 32B Instruct	Delta
MMLU (5-shot)	75.2	83.3	+8.1 Qwen
MMLU-Pro	35.5	58.6	+23.1 Qwen
BBH (3-shot)	74.9	84.5	+9.6 Qwen
ARC-c	71.4	70.4	−1.0 Gemma
HellaSwag	86.4	87.5	+1.1 Qwen
HumanEval (pass@1)	51.8	88.4	+36.6 Qwen
MBPP	53.0	84.0	+31.0 Qwen
GSM8K (CoT)	74.0	95.9	+21.9 Qwen
MATH	42.3	83.1	+40.8 Qwen
IFEval	58.0	79.5	+21.5 Qwen

Qwen 2.5 32B wins ten of eleven tracked tracks, often by a large margin. Gemma 2 closes the gap on commonsense (ARC-c, HellaSwag) but trails badly on math and code — the two domains Qwen 2.5 was explicitly retrained to dominate.

Throughput and latency on local hardware

Benchmark wins matter only if the model is fast enough to keep an interactive session usable. The reference figures below were collected on an RTX 4090 24 GB using llama.cpp b3850, Q4_K_M weights, 1,024-token output, single stream, batch size 1. Numbers from ollama.com and the BestLLMfor reference bench (see methodology) align within 3%.

Setup (Q4_K_M)	Gemma 2 27B	Qwen 2.5 32B
RTX 4090 24 GB	38 tok/s	30 tok/s
RTX 3090 24 GB	30 tok/s	23 tok/s
M2 Ultra 192 GB	22 tok/s	17 tok/s
2× RTX 3090 (TP=2)	52 tok/s	42 tok/s
Time-to-first-token (4K ctx)	0.32 s	0.41 s

Gemma 2 is roughly 25% faster on identical hardware at identical quantization — the expected outcome given the 5 B parameter delta and the sliding-window attention shortcut. For high-volume agents or chatbots where p99 latency dominates, that delta is meaningful. For one-off completions where quality dominates, it is not.

Coding, reasoning, and multilingual capability

The benchmark deltas translate into concrete behavior differences worth flagging.

Coding

Qwen 2.5 32B Instruct is competitive with the coder-specialized Qwen 2.5 Coder 32B on small tasks and significantly ahead of Gemma 2 on multi-file refactors. Gemma 2 frequently regresses on Python imports and produces hallucinated standard-library calls during repo-level work. If coding is the dominant workload, the Qwen 2.5 Coder variant is the right call; see the local code model rankings for head-to-heads against DeepSeek-Coder V2 and Codestral.

Reasoning and math

The 40-point MATH gap is the largest single delta in the scorecard. Gemma 2 27B produces fluent but arithmetically wrong chain-of-thought on multi-step word problems; Qwen 2.5 32B's RL-on-verifiable-rewards post-training pays off here. For agentic tasks involving calculation, planning, or tool selection, Qwen is the safer choice.

Multilingual

Gemma 2 is primarily English with reasonable Spanish, French, and German coverage from its 13T-token mix. Qwen 2.5 ships native coverage of 29 languages, including strong Chinese, Japanese, Korean, Arabic, and Russian performance. For Asian-language workloads, Qwen is not just better — it is materially better.

Licensing and commercial use

License nuance matters at the corporate-deployment level.

• Qwen 2.5 32B Instruct — Apache 2.0. Permissive, no use-policy carve-outs, no obligation to identify derivatives, compatible with SaaS resale and proprietary fine-tunes. The simplest license in the open-weights mid-tier.
• Gemma 2 27B — Gemma Terms of Use. Permits commercial use and derivatives, but Google reserves the right to update the prohibited-use policy and require downstream compliance. Output-attribution requirements apply, and derivative models inherit the terms. Closer to Llama 3's community license than to Apache.

For internal-only deployments the distinction is academic. For SaaS providers, the Apache 2.0 footing of Qwen 2.5 removes a category of legal review work entirely.

Installing both models with Ollama

Both ship as official Ollama tags. The fastest way to A/B them on local hardware:

1. Install Ollama 0.5.7 or newer from ollama.com/download.
2. Pull Gemma 2 27B: ollama pull gemma2:27b-instruct-q4_K_M (≈17 GB).
3. Pull Qwen 2.5 32B: ollama pull qwen2.5:32b-instruct-q4_K_M (≈20 GB).
4. Bench throughput with ollama run <model> --verbose against a fixed 1,024-token prompt.
5. Compare against the canonical numbers in the BestLLMfor model catalog — the public BestLLMfor API (CC BY 4.0) and the open-source MCP server expose the same dataset for programmatic comparison.

Verdict: which one to pick

Use case	Winner	Why
General assistant, single 24 GB GPU	Qwen 2.5 32B	+8 MMLU, +22 IFEval, 128K context
16 GB consumer GPU only	Gemma 2 27B	Only one that fits Q4 with headroom
Coding / refactor / repo work	Qwen 2.5 32B (or Coder)	+37 HumanEval, repo coherence
Math / agentic planning	Qwen 2.5 32B	+41 MATH, +22 GSM8K
Asian-language workloads	Qwen 2.5 32B	Native 29-language training mix
Highest tok/s on identical hardware	Gemma 2 27B	~25% faster, sliding-window attention
Apache-only license requirement	Qwen 2.5 32B	Apache 2.0 vs Gemma Terms
Long-document RAG (32K+)	Qwen 2.5 32B	8K context disqualifies Gemma 2

Bottom line. Qwen 2.5 32B Instruct is the better default for 2026 mid-weight local deployments — it wins on quality, context, and license, losing only on raw speed and VRAM headroom. Gemma 2 27B remains the right answer when memory is tight, when throughput matters more than benchmark scores, or when you are already invested in the Gemma family. For any new project that does not have those constraints, install Qwen 2.5 32B first and only switch if hardware forces the issue. See the BestLLMfor methodology and team for how these scores were normalized.