Llama 3.1 8B (local via Ollama) is the 4.9GB Llama that runs anywhere. Llama 3.1 was the release where Meta pushed the whole family to a 128,000 token window, and the 8B variant is still the most-downloaded local model for exactly that reason. Pricing is Free (self-hosted), and `ollama pull llama3.1:8b` fits in 8GB of VRAM or unified memory. Inside Atlas it is a general-purpose model, not a code-trained one, so keep permission mode on ask.
What is Llama 3.1 8B (local via Ollama) best at inside Atlas?
Llama 3.1 8B (local via Ollama) is best at running Atlas on hardware you already own. The command `ollama pull llama3.1:8b` is a 4.9GB download that fits in 8GB of VRAM or unified memory, and Free (self-hosted) pricing means an entire Atlas session costs nothing but electricity.
The reach is the value. An 8GB card is the floor of the consumer GPU market, and Llama 3.1 8B (local via Ollama) is the model that runs there. Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, so pairing Llama 3.1 8B with the OllamaEmbedder gives a setup where both chat and the code index stay entirely on-device. For a developer whose repository cannot touch a hosted API and whose laptop has one modest GPU, Llama 3.1 8B (local via Ollama) is often the only option that works at all.
Why did the 128,000 token window matter for Llama 3.1?
Llama 3.1 was the release where Meta pushed the whole family to a 128,000 token window, and Ollama lists llama3.1:8b with a 128K window. That is 16x what Llama 3 shipped with, which is why the 8B variant became the most-downloaded local model.
Window size is what turned a small local model from a chat toy into something an agent could use. Atlas indexes code by AST declarations using tree-sitter, not blind line windows, and it searches with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, so the context Atlas assembles is real code, not padding. A model with an 8,000 token window has nowhere to put that. Llama 3.1 8B (local via Ollama) at a nominal 128,000 tokens changed the arithmetic, at least on paper. What the paper number does not survive is the memory reality on an 8GB card.
Can an 8GB card actually use 128,000 tokens of context?
No. Actually using the full 128,000 tokens of Llama 3.1 8B (local via Ollama) on an 8GB card is not possible, because the KV cache will not fit. Declare `"limit": { "context": 32768, "output": 8192 }` in atlas.json instead of the nominal 128000.
The 4.9GB of weights is the fixed cost of Llama 3.1 8B (local via Ollama); the KV cache is the variable one, and on an 8GB card there is not much room left after the weights load. Atlas trusts limit.context and will pack retrieved code up to whatever budget you declare, so an honest 32768 produces a working session while an aspirational 128000 produces an out-of-memory failure mid-task. Run `atlas device` first to verify Ollama is installed and whether a GPU was detected, then size the context to the memory you actually have.
Is Llama 3.1 8B good enough to write code in Atlas?
Llama 3.1 8B (local via Ollama) is general-purpose, not code-trained, and on agentic edits it is beaten by a same-size coding model. Keep Atlas's permission mode on ask: an 8B model gets diffs wrong often enough that the review gate matters.
Atlas is built to absorb exactly this. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, Atlas computes a unified diff for every file edit and surfaces it for approval before writing, and Atlas snapshots file changes as git patches so edits can be diffed and rolled back. Running Llama 3.1 8B (local via Ollama) with ask-mode permissions turns a mediocre code model into a safe one: wrong diffs get rejected at the gate rather than landing on the branch. Expect to reject more of them than you would with a code-trained model of the same size.
When should you pick a different model than Llama 3.1 8B local?
Pick a code-trained model instead of Llama 3.1 8B (local via Ollama) whenever one fits your GPU. Llama 3.1 8B is general-purpose, and on agentic edits it is beaten by a same-size coding model, so the 4.9GB is buying breadth you do not need for a coding agent.
The reason Llama 3.1 8B (local via Ollama) persists despite that is availability: it is the most-downloaded local model, it is present on nearly every machine that has ever run Ollama, and it needs only 8GB. Atlas lets you switch the active model and provider on the fly with favorites and recents, so keeping Llama 3.1 8B in the same ollama provider block as a better coding model costs nothing and gives you a fallback when the larger model will not load. Set `"model": "ollama/llama3.1:8b"` and run `atlas models ollama` to confirm.
How do you keep an Atlas session fully offline with Llama 3.1 8B?
Llama 3.1 8B (local via Ollama) runs behind Atlas's OllamaEmbedder setup so both chat and the code index stay entirely on-device. Register the ollama provider in atlas.json with `@ai-sdk/openai-compatible` and baseURL `http://localhost:11434/v1`, and point the index at the local embedder.
Chat and retrieval are two separate network paths, and a local chat model with a hosted embedder still leaks the repository. Atlas closes both by using local Ollama embeddings for the code index. Once Llama 3.1 8B (local via Ollama) is registered in the ollama provider block and the index is switched to the local embedder, no part of an Atlas session touches a third-party server: not the prompts, not the diffs, not the embeddings. Verify with `atlas models ollama` and confirm hardware detection with `atlas device` before you rely on the setup.
Setup
- 01Run `ollama pull llama3.1:8b` (4.9GB).
- 02Run `atlas device` to verify Ollama is installed and whether a GPU was detected.
- 03Add the ollama custom provider to atlas.json (`@ai-sdk/openai-compatible`, baseURL `http://localhost:11434/v1`) with `llama3.1:8b` and `"limit": { "context": 32768, "output": 8192 }`.
- 04Set `"model": "ollama/llama3.1:8b"` and run `atlas models ollama` to confirm.
- 05Keep Atlas's permission mode on ask: an 8B model gets diffs wrong often enough that the review gate matters.
- 06Point the code index at Atlas's OllamaEmbedder so chat and retrieval both stay on-device.
Frequently asked questions
- how much vram do i need to run llama 3.1 8b
- The `ollama pull llama3.1:8b` download is 4.9GB and fits in 8GB of VRAM or unified memory. That leaves little room for the KV cache, so do not declare the full 128,000 token window.
- what context window should i set for llama3.1:8b in atlas
- Set `"limit": { "context": 32768, "output": 8192 }` in atlas.json. Ollama lists llama3.1:8b with a 128K window, but actually using 128,000 tokens on an 8GB card is not possible because the KV cache will not fit.
- how do i run atlas fully offline with llama 3.1 8b
- Run `ollama pull llama3.1:8b`, register the ollama provider in atlas.json with baseURL `http://localhost:11434/v1`, set `"model": "ollama/llama3.1:8b"`, and point the code index at Atlas's OllamaEmbedder so retrieval stays on-device too.
- is llama 3.1 8b good at coding
- Llama 3.1 8B is general-purpose, not code-trained, and on agentic edits it is beaten by a same-size coding model. Keep Atlas's permission mode on ask so wrong diffs get rejected at the review gate.
- how much does llama 3.1 8b cost to run in atlas
- Llama 3.1 8B (local via Ollama) is Free (self-hosted). The 4.9GB of weights runs on hardware you already own, and no tokens are billed.
- why is llama 3.1 8b the most downloaded local model
- Llama 3.1 was the release where Meta pushed the whole family to a 128,000 token window, and the 8B variant delivers that in a 4.9GB package that runs on 8GB of memory.
- why does atlas run out of memory with llama3.1:8b
- Almost always because limit.context is set too high. The 4.9GB of weights fit an 8GB card, but the KV cache for a large window does not. Start at 32768 and raise only if `atlas device` shows headroom.
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