Gemma 4 vs Llama 4 vs Qwen 3.5 Comparison — 2026 Local LLM Selection Guide

Gemma 4, Llama 4, and Qwen 3.5 are the most prominent local LLMs in 2026. Gemma 4 offers 9B and 27B parameters with 8K to 1M context length, Llama 4 provides 8B and 70B with up to 512K context, and Qwen 3.5 ranges from 0.5B to 72B with up to 128K tokens. Below is a detailed specification comparison.

Gemma 4 excels in long-context processing, Llama 4 provides high accuracy with large-scale parameters, and Qwen 3.5 offers flexible size options from lightweight to large-scale.

We compare each model's performance across major benchmarks. Gemma 4-27B achieved 51.2% on AIME 2024 and 53.8% on LiveCodeBench, matching Claude 3.5 Sonnet's performance. Llama 4-70B demonstrates strong GPQA scores as a large-scale model, while Qwen 3.5-72B shows excellent code generation with 87.3% on HumanEval.

Gemma 4-27B offers the highest performance efficiency per parameter, making it ideal for memory-constrained environments. Llama 4-70B excels at complex reasoning tasks, while Qwen 3.5 shines in code generation and multimodal support.

Gemma 4 and Qwen 3.5 use Apache 2.0 licenses, allowing unrestricted commercial use, modification, and redistribution. In contrast, Llama 4 uses the Llama Community License, which requires special permission for services exceeding 700M monthly active users (MAU).

For startups and SMEs, license differences are negligible. However, enterprises operating large-scale platforms should prefer Gemma 4 or Qwen 3.5 to avoid negotiation with Meta and reduce legal costs.

For Japanese language support, Qwen 3.5 offers the best token efficiency. Qwen 3.5 includes extensive Japanese-specific tokens, allowing it to express the same text with fewer tokens, providing advantages in inference speed and cost. Gemma 4 supports 100+ languages including Japanese, but long-form accuracy is slightly lower due to English-centric training. Llama 4 offers multilingual support but lags behind Qwen 3.5 in Japanese cultural context understanding. Recommended Models by Japanese Language Task: - Summarization/Translation: Qwen 3.5 (best token efficiency) - Long Document Reading: Gemma 4 (1M context support) - Dialogue: Llama 4 (natural responses) - Code Generation: Qwen 3.5 (Japanese comment support) For Japanese enterprises, fine-tuning Qwen 3.5 with Japanese data is most effective. Oflight provides Japanese-specific tuning support.

We compare hardware requirements for inference across models. Gemma 4-9B runs on 18GB VRAM (FP16) or 10GB (INT4 quantization), compatible with RTX 4090 or L4. Llama 4-70B requires 140GB+ VRAM, needing A100 80GB×2 or more. Qwen 3.5 ranges from 0.5B to 72B, with lightweight models runnable on CPU only.

For cost efficiency, Gemma 4-9B or Qwen 3.5-7B are optimal. INT4 quantization halves memory while maintaining performance, significantly reducing on-premise deployment costs.

Gemma 4, Llama 4, and Qwen 3.5 all support Ollama, enabling easy local execution. Ollama allows model download in minutes with `ollama pull` and provides REST API inference. Ollama Installation Examples: ```bash # Install Gemma 4 ollama pull gemma4:9b ollama pull gemma4:27b # Install Llama 4 ollama pull llama4:8b ollama pull llama4:70b # Install Qwen 3.5 ollama pull qwen3.5:7b ollama pull qwen3.5:72b ``` Ollama supports Mac, Linux, and Windows, and can run on CPU even without GPU. However, large models (27B+) strongly benefit from GPU. Ollama's OpenAI-compatible API allows seamless migration of existing LLM applications.

Each model has distinct strengths, making it crucial to select the optimal model based on use case. Below are recommendations by major scenarios. Use Case Recommendations:

For cost optimization, choose Qwen 3.5-7B; for maximum performance, choose Llama 4-70B; for balanced approach, choose Gemma 4-27B.

Gemma 4, Llama 4, and Qwen 3.5 are all free models, but hardware costs differ significantly. Comparing 5-year TCO (Total Cost of Ownership) with cloud APIs (GPT-4o, Claude 3.5 Sonnet), on-premise LLMs become advantageous beyond 1M requests annually. 5-Year TCO Comparison (1M requests/year):

Qwen 3.5-7B offers the best cost efficiency, enabling significant cost reduction from year 2 onward. However, in-house GPU management expertise is required.

The community and ecosystem behind each model are also important selection criteria. Llama 4 has the largest open LLM community supported by Meta, with tens of thousands of fine-tuned models on Hugging Face. Gemma 4 is developed by Google DeepMind, offering broad tool support across TensorFlow, JAX, and PyTorch. Qwen 3.5 is developed by Alibaba, with a strong community in China and Asia. Ecosystem Comparison: - Llama 4: Hugging Face integration, LangChain/LlamaIndex support, largest fine-tuned model collection - Gemma 4: Kaggle Models, Vertex AI integration, Google Cloud optimization - Qwen 3.5: ModelScope, Alibaba Cloud integration, extensive Chinese documentation For Japanese enterprises, balance between English documentation and Japanese community is crucial. Oflight provides technical support and deployment assistance in Japanese.

All models support quantization, which halves memory usage while maintaining near-full accuracy. Quantizing from FP16 (16-bit float) to INT4 (4-bit integer) reduces memory to 1/4 and improves inference speed by 1.5–2×. Performance Changes with Quantization:

INT4 quantization maintains ~95% performance, causing minimal practical issues. Ollama automatically provides quantized models, allowing users to benefit from optimization without special operations.

Major LLM application frameworks (LangChain, LlamaIndex, Haystack) support all three models. Via Ollama's OpenAI-compatible API, existing code can migrate with minimal changes. LangChain Usage Example: ```python from langchain_community.llms import Ollama # Using Gemma 4 llm_gemma = Ollama(model="gemma4:27b") # Using Llama 4 llm_llama = Ollama(model="llama4:70b") # Using Qwen 3.5 llm_qwen = Ollama(model="qwen3.5:72b") response = llm_gemma.invoke("What is Japan's population?") ``` For RAG (Retrieval-Augmented Generation), Gemma 4's long context is advantageous. For internal document search and contract analysis, 1M token context enables processing large reference information at once.

Q1: Which model should I choose for first-time local LLM deployment? A: Qwen 3.5-7B via Ollama is the simplest option. With RTX 4070+ GPU, it runs smoothly with excellent Japanese performance. For cost minimization, start here. Q2: Which is more performant, Gemma 4 or Llama 4? A: With equivalent parameters, performance is similar. Llama 4-70B outperforms Gemma 4-27B, but Gemma 4-27B offers better cost efficiency. Additionally, Gemma 4 has no license restrictions for large-scale services. Q3: Which model is best for Japanese-only applications? A: Qwen 3.5 is optimal. High Japanese token efficiency allows processing more Japanese text at the same inference speed. Fine-tuning with Japanese corpus further improves accuracy. Q4: What GPU is needed for 70B models? A: A100 80GB×2+ is recommended. With INT4 quantization, A100 80GB×1 or H100 80GB×1 can work, but multiple GPUs are needed for batch processing and multi-user support. Q5: Cloud API or local LLM—which is more cost-efficient? A: Cloud API for <500K requests/year, local LLM for 1M+ requests/year. For data privacy concerns, local LLM is recommended regardless of request volume. Q6: Can Ollama run multiple models simultaneously? A: Yes, launch multiple Ollama instances on different ports. For example, run Gemma 4 on port 11434 and Qwen 3.5 on port 11435, switching based on use case. Q7: Which model is easiest for fine-tuning? A: Gemma 4 and Qwen 3.5 with Apache 2.0 licenses have no restrictions and work immediately with Hugging Face standard tools (PEFT, LoRA). Llama 4 is technically similar but requires license verification for commercial deployment.

Oflight Inc. provides on-premise deployment support for Gemma 4, Llama 4, and Qwen 3.5. We select the optimal model for your business requirements and hardware environment, and provide comprehensive support from implementation with Ollama/NVIDIA NIM/vLLM, fine-tuning with Japanese data, to RAG system construction. Oflight's Local LLM Support Services: - Model selection consulting (performance, cost, license evaluation) - Environment setup with Ollama/NVIDIA NIM/vLLM - Fine-tuning with Japanese data - RAG system design and implementation - GPU optimization and memory efficiency - Operations monitoring (Prometheus/Grafana) Enterprises considering local LLM deployment, please contact us via AI Consulting Services. Initial consultation is free.