Gemma 4 Guides
Local setup walkthroughs, hardware requirement tables, and model-selection advice for people evaluating Gemma 4.
If you only read a few pages first, begin with model selection, hardware planning, and the most common setup or comparison questions.
A practical Gemma 4 E2B vs E4B guide for people choosing between the two small models, with real benchmark gaps and memory guidance.
A practical Gemma 4 26B vs 31B comparison for people deciding between the MoE sweet spot and the strongest dense model in the family.
A practical Gemma 4 VRAM calculator and model chooser built from official memory figures, so you can pick the right model before you download anything.
Model-family comparisons and version-selection guides for people deciding which Gemma 4 path to take.
Alibaba previewed Qwen 3.8 three days after Kimi K3 launched, calling it "second only to Fable 5." Problem: Qwen 3.8 doesn't have a single published benchmark yet. Here's what's actually confirmed on both sides before you pick one.
Moonshot AI's Kimi K3 launched July 16, 2026 as a 2.8-trillion-parameter open-weight model that beats Claude Opus 4.8 on several benchmarks. Here is what it actually is, what's still missing, and how to try it today.
Kimi K3 beats Claude Opus 4.8 on price and most benchmarks, but Claude Fable 5 still leads on frontier engineering. Here's exactly where each model wins, sourced from Artificial Analysis and each vendor's own numbers.
GLM 5.2 is the open-weights coding giant with a 1M context window; Gemma 4 is the local-friendly family from 3GB to 20GB VRAM. Here is how to choose.
E stands for effective parameters, A stands for active parameters. They describe completely different architectures. Here is how to pick the right one for your machine.
For most local setups, Q4_K_M is the right starting point. Q8 helps specifically with multi-step reasoning and long code tasks when memory is not a constraint. There is also a QAT option that changes the math entirely.
Kimi K2.6 and GLM-5.1 were two major April 2026 open-weight releases, but fresh evaluations should now include Kimi K2.7 Code and GLM-5.2.
Decode Gemma 4's updated five-model family, compare the main variants, and find the right model for your hardware before you download anything.
Gemma 4 vs Qwen is not a one-line winner question. This guide helps you decide based on workflow, hardware, deployment, and ecosystem fit.
Practical setup walkthroughs for Ollama, LM Studio, llama.cpp, Google AI Studio, and adjacent Gemma 4 workflows.
Kimi K3 isn't discontinued or actually "sold out" — Moonshot paused new subscription signups after demand maxed out its GPU capacity within 48 hours of launch. Here's what that means if you're trying to get access right now.
Kimi K3 hit #1 on LMArena's Frontend Code Arena and beat Claude Opus 4.8 on the Artificial Analysis Intelligence Index within a day of launch. Here's what the numbers actually say — and where they don't tell the whole story.
Kimi K3's API is $3 per million input tokens and $15 per million output — but the always-on thinking mode means you're paying for reasoning tokens on every single call. Here's what that actually costs.
Complete guide to GLM 5.2 pricing in 2026: API token costs, GLM Coding Plan subscription tiers (Lite/Pro/Max/Team), OpenRouter rates, and how to get free access.
GLM 5.2 launched on June 13, 2026 as Zhipu AI's open-weight flagship — 744B MoE parameters, a 1-million-token context window, MIT license, and benchmark scores that rival closed-source frontier models at roughly one-sixth the API cost. Here is everything you need to know.
GLM 5.2 is free to download and self-host under the MIT license, and free to try via Cloudflare Workers AI and the z.ai web chat. This guide covers every free option and where paid plans kick in.
GLM-5.2 is available in Ollama via the glm-5.2:cloud tag — one command gets you a 976K-context coding model without managing a 744B-parameter download yourself.
Both LM Studio's llama.cpp and MLX engines fail to load DiffusionGemma in June 2026. Here is what the errors mean, where they are tracked, and which tools actually work.
DiffusionGemma works with llama.cpp — but you need the llama-diffusion-cli binary from PR #24423, not standard llama-cli. Here is exactly how to build or download it and get it running.
The gemma4 and diffusion-gemma architecture errors have different causes and different fixes. Treating them the same way will waste your time.
Current Kimi API pricing for K2.6 and K2.7 Code, what cached input means, how rate limit tiers work, and what to verify before budgeting a coding or agent workflow.
Everything developers need from the moonshotai/Kimi-K2.6 model card: what the weights actually include, how to deploy with vLLM or SGLang, and how to decide between self-hosting and the official API.
Kimi K2.6 arrived on April 20, 2026 as an open-weight agentic coding model with 256K context, native vision and video input, and an aggressive agent-swarm story. This review breaks down what's real, what's marketing, and who should actually switch.
A practical guide to running Kimi K2.6 through Ollama using the official kimi-k2.6:cloud entry — setup commands, coding-agent integrations, and what cloud-backed Ollama means for your workflow.
Muse Spark is Meta's new AI model from Meta Superintelligence Labs. This guide covers capabilities, Contemplating mode, benchmarks, and what to watch before you commit to it.
A practical answer to whether llama.cpp supports Gemma 4, with the official GGUF links, current support status, and what 'supported' really means.
A clear answer to whether LM Studio supports Gemma 4, with the supported model list, minimum memory, and practical setup expectations.
A practical answer to whether Unsloth supports Gemma 4, covering local run support, fine-tuning support, and the model-specific caveats that matter.
A practical Gemma 4 on iPhone guide covering iOS setup, model choice, device fit, offline use, and what performance to expect.
Use this Gemma 4 API guide to build a local OpenAI-compatible endpoint, test it quickly, and choose the right runtime for your workflow.
A practical Gemma 4 on Windows setup guide covering hardware checks, Ollama, LM Studio, model choice, and the most common Windows issues.
Use this step-by-step guide to fine-tune Gemma 4 with Unsloth, choose the right model for your hardware, and export the result for Ollama, llama.cpp, or LM Studio.
Use this Gemma 4 GGUF download guide to pick a trusted source, choose the right file, and get from download to first local response with less guesswork.
Use this Gemma 4 review to understand the model family, the most important Gemma 4 benchmark numbers, and the real deployment tradeoffs before you commit.
If you are asking what is Gemma 4, this guide explains the release, model sizes, context limits, licensing, and the easiest ways to get started.
Google AI Studio is one of the fastest ways to evaluate hosted Gemma 4 access, especially if you are not ready to commit to local setup yet.
Use this guide to understand where Unsloth fits into a Gemma 4 workflow and what to decide before you jump into tuning.
A practical LM Studio guide for Gemma 4, focused on model choice, hardware fit, first-run workflow, and what to check before you blame the model.
Get Gemma 4 running in llama.cpp: pick the right GGUF for your hardware, build with CUDA/Metal/CPU support, and run 12B–31B models locally. Copy-paste commands included.
Hardware requirement pages and machine-specific planning guides so you can avoid downloading the wrong model first.
GLM 5.2 is a 744B-parameter MoE model released under MIT license. Here is everything you need to know about the hardware required to run it locally.
A focused Gemma 4 26B A4B VRAM requirements guide with exact GGUF sizes, planning ranges, and why the 26B is the local sweet spot.
A focused Gemma 4 31B VRAM requirements guide with exact GGUF sizes, planning ranges, and honest advice on what hardware makes sense.
A focused Gemma 4 E2B VRAM requirements guide with exact file sizes, practical planning ranges, and honest advice on when E2B is the right fit.
A focused Gemma 4 E4B VRAM requirements guide with exact sizes, planning ranges, and practical advice for laptop-class local AI.
If you are asking whether a Mac mini can run Gemma 4, the real answer depends on which Gemma 4 model you mean and what kind of experience you expect.
A practical Gemma 4 hardware guide with the official approximate memory table and simple advice on which model to try first.
The fastest path from zero to a working Gemma 4 local run: the right tag, the right hardware check, and the right command — without wasting time on the wrong model.