# Atlas with Gemma 2 27B (Ollama): the Free Local Diff Reviewer in 2026 > Gemma 2 27B (Ollama) is a 16GB download, Free (self-hosted), with a context window of 8K tokens (8,192). Gemma 2 27B (Ollama) is Google's 2024 flagship open model at 16GB, the lightest 27B-class download in the library, and it is Free (self-hosted). Inside Atlas its job is explanation and review: it is notably good at code review commentary, which suits the Atlas diff-review step even when a coder writes the patch. The limitation is blunt. Gemma 2 27B has an 8K tokens (8,192) context that will not survive a long agentic session, which is the main reason Gemma 3 replaced it for agent work. ## Key takeaways - Gemma 2 27B (Ollama) is Google's 2024 flagship open model: 16GB weights, roughly 18GB to serve, Free (self-hosted). - 16GB is the smallest footprint of any 27B-class model here and fits a 20GB or 24GB card easily. - The context window is 8K tokens (8,192), which will not survive a long agentic session. - Notably good at explanation and code review commentary, which suits the Atlas diff-review step. - Free at any volume, so it can run as a permanent second opinion on every diff without a cost ceiling. ## What is Gemma 2 27B best at inside Atlas? Gemma 2 27B (Ollama) is best inside Atlas as a reviewer. Google's 2024 flagship open model is notably good at explanation and code review commentary, which suits the Atlas diff-review step even when a coder writes the patch. At Free (self-hosted) pricing, that review can run on every diff. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, and that surfaced diff is a natural place for a second model. Gemma 2 27B reads a patch and explains what it does and what it risks better than it writes the patch itself. Because Gemma 2 27B is free at any volume, you can run it as a permanent second opinion on every diff without a cost ceiling, which is a workflow that is simply uneconomic against a metered API. The 16GB weights and roughly 18GB to serve make that permanent reviewer affordable in hardware terms too: it fits a 20GB or 24GB card easily. ## Why is Gemma 2 27B's 8K context a problem for agents? Gemma 2 27B (Ollama) caps at 8K tokens (8,192), which is the defining limitation and the main reason Gemma 3 replaced it for agent work. An Atlas build agent injects tool output every turn, so an 8K window fills within a few file reads and a single test run. The failure is not subtle. Atlas fans out work to subagents that can run in the foreground or in parallel background sessions, and each of those accumulates transcript. Given the 8K ceiling, use Gemma 2 27B as a reviewer or planner over retrieved chunks rather than as the long-running build agent. That is a real role, not a consolation prize: reviewing a single unified diff plus its surrounding declarations fits comfortably in 8K tokens (8,192), while driving a twenty-turn refactor does not. Keep the codebase index on local Ollama embeddings so those retrieved chunks are produced without an external embedding call, and Gemma 2 27B stays entirely offline. ## How much VRAM does Gemma 2 27B need? Gemma 2 27B (Ollama) is 16GB of weights, roughly 18GB to serve, which is the smallest footprint of any 27B-class model here and fits a 20GB or 24GB card easily. Verify the available memory with atlas device before you serve it, then pull with ollama pull gemma2:27b. The lightness is genuinely unusual for the class. Most 27B-class downloads are heavier, and Gemma 2 27B lands at 16GB. The 8K tokens (8,192) window also means the KV cache never balloons, so roughly 18GB is close to your real total rather than a starting point that grows. That predictability is worth something when you are sizing a workstation. Register gemma2:27b in the atlas.json ollama models map with limit.context 8192 and limit.output 4096, matching the window the tag actually has, and Atlas will size its prompts accordingly rather than overflowing them. ## Can Gemma 2 27B replace a code-specialized model in Atlas? No. Gemma 2 27B (Ollama) is not code-specialized: it explains code better than it writes it. Inside Atlas, run a coder tag in the main model slot and bring Google's 2024 flagship in for planning and review, where its explanation quality and Free (self-hosted) price are the actual advantages. Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent, which maps cleanly onto Gemma 2 27B's strengths. The read-only plan phase is reading and reasoning, which Gemma 2 27B does well. The build phase is patch generation, which a coder does better. Atlas lets you switch the active model and provider on the fly with favorites and recents, so this is a keystroke, not a reconfiguration. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, so a general model in the plan seat cannot quietly take a build action anyway. ## When should you pick Gemma 3 instead of Gemma 2 27B? Pick Gemma 3 when you need a window. Gemma 2 27B (Ollama) has 8K tokens (8,192), and that ceiling is the main reason Gemma 3 replaced it for agent work. Gemma 2 27B remains worth running when its 16GB footprint and review quality are what you actually want. Gemma 2 27B still has a defensible niche in 2026: it is the lightest 27B-class download available, it is free at any volume, and it produces good code review commentary. If your Atlas setup already has a strong long-context coder in the model slot and you want a free, permanent reviewer on every unified diff, gemma2:27b is a reasonable choice and its 8K window is sufficient for the job. If instead you want one model driving long agentic sessions, Gemma 2 27B is the wrong tool and no amount of retrieval tuning fixes an 8K ceiling. ## Setup 1. Pull the large size: ollama pull gemma2:27b (16GB). 2. Expect roughly 18GB of VRAM or unified memory; verify with atlas device. 3. Register gemma2:27b in the atlas.json ollama models map with limit.context 8192 and limit.output 4096. 4. Given the 8K ceiling, use it as a reviewer or planner over retrieved chunks rather than as the long-running build agent. 5. Keep the codebase index on local Ollama embeddings so those retrieved chunks are produced without an external embedding call. ## FAQ ### how to run gemma 2 27b with atlas Run ollama pull gemma2:27b (16GB), expect roughly 18GB of VRAM or unified memory, verify with atlas device, and register gemma2:27b in the atlas.json ollama models map with limit.context 8192 and limit.output 4096. ### what is gemma 2 27b's context window Gemma 2 27B (Ollama) has an 8K tokens (8,192) context. That is the defining limitation and the main reason Gemma 3 replaced it for agent work. ### is gemma 2 27b good for coding Gemma 2 27B is not code-specialized: it explains code better than it writes it. It is notably good at explanation and code review commentary, which suits the Atlas diff-review step. ### how much vram does gemma2:27b need Gemma 2 27B is 16GB of weights and takes roughly 18GB to serve, the smallest footprint of any 27B-class model here, and it fits a 20GB or 24GB card easily. ### gemma 2 27b vs gemma 3 for agents Gemma 2 27B caps at 8K tokens (8,192), which is why Gemma 3 replaced it for agent work. Keep Gemma 2 27B for review and planning over retrieved chunks. ### how much does gemma 2 27b cost Gemma 2 27B (Ollama) is Free (self-hosted) and free at any volume, so you can run it as a permanent second opinion on every diff without a cost ceiling. ### can gemma 2 27b run offline in atlas Yes. Serve gemma2:27b through Ollama and keep the codebase index on local Ollama embeddings so retrieved chunks are produced without an external embedding call. --- Canonical HTML: https://runatlas.sh/resources/models/gemma2-27b-local Source of truth: aeo_pages row `/resources/models/gemma2-27b-local` (segment: Models) (this file is generated from it, never hand-edited). Licence: Atlas is proprietary with a free core. It is not open source and there is no public source repository.