Gemma 4 12B (Ollama) is an odd and useful data point: a 7.6GB download, smaller than the 9.6GB E4B default, that doubles the context to 256K tokens (262,144). Inside Atlas it is the tag to choose when you want the longest Gemma window that still fits a mid-range GPU, at Free (self-hosted) pricing and roughly 10GB to serve. The catch is the cache: a 256K KV cache dwarfs the 10GB weight floor, so the advertised window is only reachable with substantial extra memory.
Why is Gemma 4 12B smaller than Gemma 4 E4B but twice the context?
Gemma 4 12B (Ollama) is a 7.6GB download, smaller than the 9.6GB E4B default, yet it doubles the context to 256K tokens (262,144). That inversion is worth knowing before you pick a Gemma 4 tag, because the intuitive assumption that a bigger file buys a bigger window is wrong here.
For an Atlas user the practical rule is simple: if you want the longest Gemma window that still fits a mid-range GPU, gemma4:12b is the tag. It offers twice the window of the E4B tag from a smaller file, and roughly 10GB to serve, so a quarter-million-token window runs on a 12GB consumer card. Gemma 4 12B also sits in the same Gemma 4 tag family as the 26b and 31b, so scaling up later needs no config rewrite beyond the tag and the context. That family continuity is a real convenience when a workstation upgrade arrives and you want to move from 12B to 31B without redoing the atlas.json provider block.
Can you actually use all 256K tokens with Gemma 4 12B?
Not on 10GB. A 256K KV cache dwarfs the 10GB weight floor of Gemma 4 12B (Ollama), so the advertised 256K tokens (262,144) window is only reachable with substantial extra memory. Start Ollama with a conservative num_ctx and raise it toward 256K only while watching resident memory.
The weight figure and the usable window are two different budgets, and Gemma 4 12B makes that gap unusually visible because the window is so large relative to the file. The honest way to run it in Atlas is incrementally: pick a num_ctx that your card sustains, verify with atlas models ollama that the model resolves, then push the number up while watching memory. Because Gemma 4 12B is Free (self-hosted), that tuning loop costs nothing but time. Even a partial slice of 256K tokens (262,144) is far more than the 8K windows on the older Gemma tags, so the model is useful long before you reach the advertised ceiling.
What is Gemma 4 12B best at inside Atlas?
Gemma 4 12B (Ollama) is best inside Atlas at long-context planning sessions. Set model to ollama/gemma4:12b when a task requires reading widely before acting, because 256K tokens (262,144) is enough to hold a large slice of a repository plus the tool transcript that follows.
Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent, and the plan agent is where a quarter-million-token window pays for itself. Gemma 4 12B is general-purpose rather than code-tuned, so use it to plan and review, not to write every patch. Atlas fans out work to subagents that can run in the foreground or in parallel background sessions, and a long-window planner keeps those subagents coherent by holding the full picture that they each see only a slice of. Atlas lets you switch the active model and provider on the fly with favorites and recents, so handing the build phase to a coder tag is a keystroke.
Does a 256K window need a cloud index?
No. Index the repo with Ollama embeddings so you can fill Gemma 4 12B's 256K tokens (262,144) window from a private codebase with zero network egress. Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, which is what makes a huge local window a privacy feature rather than a liability.
A large window is only as useful as what you put in it, and what you put in it comes from retrieval. Atlas indexes code by AST declarations using tree-sitter, not blind line windows, so a 256K prompt built for Gemma 4 12B contains whole functions and classes rather than sliced fragments. Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, which orders those declarations sensibly even when you can afford hundreds of them. Run the whole chain on Ollama, and a private repository can fill a quarter-million-token prompt without a single byte leaving the machine.
When should you pick a different Gemma 4 tag?
Move up from Gemma 4 12B (Ollama) when 10GB of served weights is not enough capacity for your work, since the 26b and 31b tags carry the same 256K tokens (262,144) window with more parameters. Stay on gemma4:12b when a 12GB consumer card is what you have.
Gemma 4 12B is the value tag of the Gemma 4 line: a 7.6GB download, the full 256K window, and roughly 10GB to serve. It is the wrong pick if you want code-tuned generation, because it is general-purpose rather than code-tuned, so use it to plan and review, not to write every patch. It is also the wrong pick if you have a 24GB card idle, because the larger tags in the same family give more capacity at the same window. Since all of them are Free (self-hosted), the constraint is memory, not budget.
Setup
- 01Pull it: ollama pull gemma4:12b (7.6GB).
- 02Add gemma4:12b to the ollama models map in atlas.json with limit.context 262144 and limit.output 8192.
- 03Start Ollama with a conservative num_ctx and raise it toward 256K only while watching resident memory.
- 04Verify with atlas models ollama, then set model to ollama/gemma4:12b for long-context planning sessions.
- 05Index the repo with Ollama embeddings so you can fill that 256K window from a private codebase with zero network egress.
Frequently asked questions
- how to set up gemma 4 12b with atlas
- Run ollama pull gemma4:12b (7.6GB), add gemma4:12b to the ollama models map in atlas.json with limit.context 262144 and limit.output 8192, verify with atlas models ollama, then set model to ollama/gemma4:12b.
- what is gemma 4 12b's context window
- Gemma 4 12B (Ollama) has a 256K tokens (262,144) context, twice the window of the E4B tag, from a smaller 7.6GB download.
- why is gemma4:12b smaller than gemma4:e4b
- Gemma 4 12B is a 7.6GB download while the E4B default is 9.6GB, yet Gemma 4 12B doubles the context to 256K tokens (262,144). It is an odd and useful data point in the Gemma 4 tag list.
- how much vram do i need to use gemma 4 12b's full context
- Gemma 4 12B takes roughly 10GB to serve, but a 256K KV cache dwarfs that weight floor. Start Ollama with a conservative num_ctx and raise it toward 256K while watching resident memory.
- is gemma 4 12b good for writing code
- Gemma 4 12B is general-purpose rather than code-tuned, so use it to plan and review, not to write every patch. Hand the build phase to a coder tag inside Atlas.
- is gemma 4 12b free to use
- Yes. Gemma 4 12B (Ollama) is Free (self-hosted). A 7.6GB download and roughly 10GB of memory to serve are the only costs.
- can i upgrade from gemma 4 12b to a larger tag later
- Yes. Gemma 4 12B is part of the same Gemma 4 tag family as the 26b and 31b, so scaling up later needs no config rewrite beyond the tag and the context.
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