New Trends in Local LLMs: Gemma 4 and Qwen 3.5 Mark the “Critical Point” of Japanese Reasoning

1. Introduction: Local LLMs Shift from “Substitutes” to “Primary Choices”

Over the past few months, the landscape surrounding local LLMs (Large Language Models) has entered a “paradigm shift” that goes beyond mere technical progress. Previously, local models were often seen as mere “substitutes”—compromises accepted for the sake of privacy or cost reduction when cloud-based AI performance was out of reach.

However, with the arrival of Google’s “Gemma 4” and Alibaba’s “Qwen 3.5,” the power balance has shifted dramatically. In specific tasks—particularly programming and structured data processing—these models are beginning to demonstrate capabilities that threaten the dominance of the GPT-4 class. Combined with the refinement of execution environments like Ollama, “owning the source of thought on your own hardware” has become one of the most creative choices an engineer can make.

2. Gemma 4: Google’s Achievement in “Native Japanese” Fluency

The first thing that surprises you when deploying Gemma 4 in an Ollama environment is its overwhelming “linguistic fluency.” This isn’t just about having a large vocabulary. It interprets the subtle fluctuations of Japanese context and the delicate use of particles as if it were a native speaker.

• Refined Architecture: By further advancing the distillation technology from the flagship “Gemini” models, it achieves context comprehension comparable to giant models despite its smaller parameter size (7B–9B class).
• Tokenizer Optimization: Japanese tokenization is extremely efficient, contributing to both improved generation speed (Tokens per second) and semantic accuracy.
• Ease of Operation: With a single command, ollama run gemma4, you can get responses with almost no perceived latency, even on consumer-grade GPUs with 12GB of VRAM.

Gemma 4 has effectively liberated the local environment from the “stress of dialogue.”

3. Qwen 3.5’s “Reasoning”: The Light and Shadow of Visualized Thought

On the other hand, Alibaba Cloud’s “Qwen 3.5” stands out as a pioneer of the current “Reasoning-type” model trend. The contents of the <thought> tags output before the final answer serve as a record of the AI’s “trial and error” process.

However, this phenomenon of “leaking thoughts” has exposed unique challenges in the Japanese language environment.

During logical construction steps, the internal processing can clash between English and Japanese logic, sometimes resulting in “thought loops” where the model repeats the same logic infinitely before reaching an answer. It could be said that in its pursuit of logical consistency, the model overflows while failing to fully process the “non-linear context” inherent in Japanese.

Conversely, this characteristic demonstrates unparalleled strength in mathematical proofs and complex code debugging. Because the process is visualized, users can immediately identify “where the AI misunderstood.” This enables a high level of “collaborative debugging” that was impossible with traditional black-box AIs.

4. Spec and Use Case Comparison

Organizing the characteristics of both models makes the choice clear.

5. On-Site Implementation: “Key Points” for Maximizing Performance

To extract the true value of a local LLM, understanding “Quantization” is just as important as choosing the model itself.

Many users opt for “4-bit quantization (q4_K_M)” to save memory, but for reasoning-specialized models like Qwen 3.5, excessive quantization can be fatal. If weight precision is dropped too low, the chain of reasoning can collapse midway, increasing the probability of the aforementioned “infinite loops.”

Advice from a Tech Evangelist: If you have VRAM to spare, consider running at least “q6_K,” or ideally “FP16.” Especially when delegating complex programming tasks, this difference in precision becomes the boundary between “usable” and “unreliable.” Additionally, when using Ollama, strictly managing your resources by setting the OLLAMA_NUM_GPU environment variable to prevent compute from leaking to the CPU is proper “etiquette” for local environments.

6. FAQ: A Prescription for Implementing Local LLMs

Q: How is the performance on Apple Silicon (M1/M2/M3)? A: Ollama is designed to maximize Apple Silicon’s unified memory. Gemma 4, in particular, runs surprisingly comfortably on models with 16GB of memory or more. Macs have now become the most accessible “AI workstations” in the world.

Q: Can I hide Qwen’s reasoning process? A: While it is possible to hide it via system prompts or UI settings, doing so is equivalent to discarding the greatest weapon of a “Reasoning model.” If you dislike the redundancy of the process, it is wiser to choose Gemma 4 from the start.

Q: What are the legal risks for use in commercial projects? A: Both models are provided under open licenses (Gemma Commercial License, Qwen License), but there may be restrictions regarding user counts or specific use cases. Please ensure you check the latest LICENSE file in each official repository.

7. Conclusion: In 2026, We Tame “Intelligence” Locally

The “overwhelming quality of dialogue” shown by Gemma 4 and the “transparency of thought” presented by Qwen 3.5 are not mutually exclusive; they are complementary forces in our workflow.

Entrust routine text processing and creative writing to Gemma 4, and partner with Qwen 3.5 for engineering work that demands rigorous logic. This “selective use of models” will become the core of tech literacy moving forward.

There is no longer a need to surrender your data to the cloud to utilize powerful AI. Launch Ollama now and experience the “explosion of intelligence” in your local environment. Beyond that lies the freedom of creativity, unbound by anyone.

This article is also available in Japanese.