DeepSeek Coder 6.7B (Ollama) is the original 2023 DeepSeek Coder line, still one of the leanest code models that gives useful completions. The download is 3.8GB, serving it takes roughly 6GB, context is 16K tokens (16,384), and pricing is Free (self-hosted). Inside Atlas it is dated next to Qwen2.5-Coder, but it starts fast and is a reasonable fallback on thin hardware.
What hardware can run DeepSeek Coder 6.7B (Ollama) with Atlas?
DeepSeek Coder 6.7B (Ollama) is a 3.8GB download that runs on a 6GB GPU, which is old-laptop territory. Serving it takes roughly 6GB total, so a machine that cannot host a 7B Qwen2.5-Coder can often still host DeepSeek Coder 6.7B and drive Atlas locally.
The whole case for DeepSeek Coder 6.7B (Ollama) in 2026 is the hardware floor. Pull it with `ollama pull deepseek-coder:6.7b` (3.8GB) and it lands on a 6GB card that newer models will not fit. For a developer whose machine is a few generations old, that is the difference between running Atlas locally and not running it locally at all. DeepSeek is the vendor, the line dates to 2023, and neither fact stops it from producing useful completions on hardware that has nothing else to run.
Why does DeepSeek Coder 6.7B do well at fill-in-the-middle completion?
DeepSeek Coder 6.7B (Ollama) was trained code-first from scratch rather than fine-tuned from a general model, which shows on fill-in-the-middle style completion. The 2023 DeepSeek Coder line was a code model from the first token of pretraining, not a chat model taught to code afterward.
Training provenance leaves fingerprints. DeepSeek Coder 6.7B (Ollama) reaches for code structure by default rather than reaching for prose and then formatting it. Base and instruct tags ship at the same size, so you can drive it as a raw completion engine or a chat model depending on what you want from Atlas. For an agent that computes a unified diff for every file edit and surfaces it for approval before writing, the instruct tag is the practical choice, but the base tag exists if you have a completion-shaped use for it.
Is the 16K context of DeepSeek Coder 6.7B enough for Atlas?
DeepSeek Coder 6.7B (Ollama) has 16K tokens (16,384), which is tight for an agent that injects diffs and tool output on every turn. Register deepseek-coder:6.7b with limit.context 16384 and limit.output 4096, and do not overstate the window: this line really is 16K.
Overstating the context is the most common configuration error with DeepSeek Coder 6.7B (Ollama), because most 2026-era local models advertise far more. The 2023 line really is 16K tokens (16,384), and declaring a bigger limit.context in atlas.json produces silent truncation instead of an error. Atlas fills that window quickly: retrieved AST chunks, tool output, and a unified diff per edit all land in the prompt. Plan for compaction on anything beyond a focused single-file change.
How should Atlas permissions be set with DeepSeek Coder 6.7B?
Set Atlas permissions to ask rather than allow with DeepSeek Coder 6.7B (Ollama), since a 2023-era model is more likely to propose a bad tool call. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, so ask turns a bad call into a prompt.
The value of an approval gate scales inversely with model quality, and DeepSeek Coder 6.7B (Ollama) predates the current generation. Running Atlas on ask means each proposed tool call stops for a human before it executes. Atlas also snapshots file changes as git patches so edits can be diffed and rolled back, which gives you a second recovery path if a questionable edit gets through. On thin hardware with a dated 3.8GB model, both of those guardrails are worth the extra keystrokes.
Should you use DeepSeek Coder 6.7B or Qwen2.5-Coder 7B?
A 7B Qwen2.5-Coder will usually beat DeepSeek Coder 6.7B (Ollama) at nearly the same footprint, so choose DeepSeek Coder 6.7B only when its 3.8GB pull and roughly 6GB serving cost are the deciding constraint on a 6GB GPU.
The honest comparison is not close on quality: DeepSeek Coder 6.7B (Ollama) predates the current generation and its 16K tokens (16,384) window is narrower than what the Qwen2.5-Coder line offers. What DeepSeek Coder 6.7B still owns is the low end. If your card is 6GB, the newer model does not fit and the older one does, and a running agent beats a better model you cannot load. Point the codebase index at the local Ollama embedder so retrieval quality does not depend on shipping code to a cloud embedding API, and take the free local setup.
Setup
- 01Pull the mid size: ollama pull deepseek-coder:6.7b (3.8GB).
- 02In atlas.json define the ollama provider on @ai-sdk/openai-compatible with baseURL http://localhost:11434/v1.
- 03Register deepseek-coder:6.7b with limit.context 16384 and limit.output 4096; do not overstate the window, this line really is 16K.
- 04Set Atlas permissions to ask rather than allow, since a 2023-era model is more likely to propose a bad tool call.
- 05Point the codebase index at the local Ollama embedder so retrieval quality does not depend on shipping code to a cloud embedding API.
Frequently asked questions
- can i run a coding model on a 6gb gpu
- Yes. DeepSeek Coder 6.7B (Ollama) is a 3.8GB download that runs on a 6GB GPU, which is old-laptop territory. Serving it takes roughly 6GB, so it fits where a larger 2026-era coder will not.
- what context window does deepseek coder 6.7b have
- DeepSeek Coder 6.7B (Ollama) has 16K tokens (16,384). Register it in atlas.json with limit.context 16384 and limit.output 4096, and do not overstate the window: the 2023 DeepSeek Coder line really is 16K.
- deepseek coder 6.7b vs qwen2.5-coder 7b
- A 7B Qwen2.5-Coder will usually beat DeepSeek Coder 6.7B (Ollama) at nearly the same footprint. DeepSeek Coder 6.7B predates the current generation, but its 3.8GB pull fits on a 6GB GPU, which is its remaining advantage.
- is deepseek coder 6.7b still worth using in 2026
- DeepSeek Coder 6.7B (Ollama) is dated next to Qwen2.5-Coder, but it starts fast and is a reasonable fallback on thin hardware. It is one of the leanest code models that still gives useful completions, at 3.8GB and Free (self-hosted).
- what is the difference between deepseek coder base and instruct
- Base and instruct tags of DeepSeek Coder 6.7B (Ollama) ship at the same size, so you can drive it as a raw completion engine or a chat model. For Atlas tool calls and diff approval, the instruct tag is the practical choice.
- should i set atlas permissions to ask or allow with an older model
- Set Atlas permissions to ask rather than allow with DeepSeek Coder 6.7B (Ollama), since a 2023-era model is more likely to propose a bad tool call. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs.
- why is deepseek coder good at fill in the middle
- DeepSeek Coder 6.7B (Ollama) was trained code-first from scratch rather than fine-tuned from a general model, which shows on fill-in-the-middle style completion. The line dates to 2023 and was a code model from the start.
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