# Atlas with Nemotron 70B (Ollama) in 2026 > Nemotron 70B (Ollama) offers a substantial 128K token context window, making it a powerful, free (self-hosted) option for Atlas. Nemotron 70B (Ollama) is an excellent choice for Atlas developers in 2026 who prioritize strict instruction adherence and a generous context window without incurring direct model costs. This 70B-class model, tuned by NVIDIA, provides a 128K token context window and is available free through self-hosting, making it ideal for complex, long-running agent tasks where precision is key. ## Key takeaways - Nemotron 70B (Ollama) offers a 128K token context window for extensive code understanding. - It is Free (self-hosted), eliminating direct model costs for Atlas users. - NVIDIA's tuning ensures precise instruction adherence, crucial for agent reliability. - Requires significant workstation hardware: 48GB VRAM or 64GB unified memory to serve its 43GB weights. - While powerful, a 32B code specialist might offer better raw diff quality for specific coding tasks. - Atlas supports local Ollama embeddings, maintaining full control over your code index. ## Why choose Nemotron 70B (Ollama) for Atlas in 2026? In 2026, Nemotron 70B (Ollama) stands out for Atlas users seeking precise instruction adherence, thanks to NVIDIA's specific tuning. This 70B-class model offers a substantial 128K token context window, making it ideal for complex coding tasks. Nemotron 70B (Ollama) is NVIDIA's Llama-3.1-Nemotron-70B-Instruct, a model specifically tuned with reward modeling to follow instructions precisely rather than to be agreeable. This distinction is crucial within an agent loop like Atlas, where a model that quietly reinterprets instructions can lead to suboptimal outcomes compared to one that clearly refuses, ensuring more predictable agent behavior. With 43GB of weights, this 70B-class model provides a 128K token context window (131,072 tokens), allowing it to handle extensive codebases, complex problem descriptions, and maintain long conversational histories within Atlas. It runs effectively on workstation hardware, specifically requiring a 48GB VRAM card or a 64GB unified-memory machine for optimal performance. A key advantage is that only one size of Nemotron 70B ships, eliminating any confusion regarding model tags; the "70b" tag consistently refers to this specific, powerful model, simplifying deployment and configuration within Atlas. ## What are the tradeoffs of Nemotron 70B (Ollama) with Atlas? While Nemotron 70B (Ollama) is Free (self-hosted), its 43GB weight size demands workstation-grade hardware, specifically a 48GB VRAM card or 64GB unified memory. This 70B general instruct model might not always outperform a 32B code specialist on raw diff quality. The primary tradeoff for Nemotron 70B (Ollama) is its significant hardware requirement. Serving the model requires roughly 45GB of memory, which necessitates a dedicated workstation with at least a 48GB VRAM graphics card or a machine equipped with 64GB of unified memory. This substantial footprint makes it unsuitable for typical laptop deployments or environments with limited resources. Furthermore, while Nemotron 70B is a highly capable general instruct model, it is not specifically trained as a code specialist. Consequently, a smaller, dedicated 32B code specialist model might often achieve superior raw diff quality for specific coding tasks, potentially using only a third of the memory required by Nemotron 70B. Developers must weigh the benefits of its robust instruction adherence and expansive 128K token context window against these considerable hardware and specialization considerations when integrating it with Atlas. ## When should I consider other models over Nemotron 70B (Ollama) for Atlas? If your hardware setup in 2026 cannot comfortably serve the 43GB Nemotron 70B (Ollama) weights, or if raw diff quality for specific code tasks is paramount, a different model might be more suitable. A 32B code specialist, for example, often requires a third of the memory. Developers should consider alternative models if their local machine does not meet the substantial hardware requirements of Nemotron 70B (Ollama), which needs approximately 45GB of free VRAM or unified memory. For users without a 48GB VRAM card or a 64GB unified-memory workstation, attempting to run this model will lead to performance issues, slow response times, or outright failure, hindering the Atlas agent's efficiency. Additionally, if the primary use case within Atlas involves highly specialized code generation or refactoring where the absolute best raw diff quality is the top priority, a dedicated code specialist model, even one with a smaller parameter count like 32B, could offer better results. These specialized models are often fine-tuned on vast code datasets and can sometimes outperform general instruct models in their niche, while also consuming significantly less memory. Atlas allows switching models on the fly with favorites and recents, so experimenting with different options is straightforward to find the best fit for your specific tasks and hardware. ## Setup 1. Pull it: ollama pull nemotron:70b (43GB, aliased to :latest and the only size published) 2. Confirm roughly 45GB of free VRAM or unified memory with atlas device before serving 3. Add nemotron:70b to the ollama models map in atlas.json with limit.context 131072 and limit.output 8192 4. Set model to ollama/nemotron:70b and small_model to a 3B tag so cheap Atlas calls do not evict 43GB of weights 5. Keep the codebase index on Ollama embeddings so a model chosen for strict instruction adherence is also fed by a retrieval path you fully control ## FAQ ### What is the context window for Nemotron 70B (Ollama) in Atlas? Nemotron 70B (Ollama) provides a substantial 128K tokens (131,072) context window, enabling Atlas to process and understand very large codebases and complex instructions. ### How much does it cost to use Nemotron 70B (Ollama) with Atlas? Nemotron 70B (Ollama) is Free (self-hosted). There are no direct model costs, though it requires significant local hardware investment to run. ### What hardware is required to run Nemotron 70B (Ollama) locally for Atlas? To serve Nemotron 70B (Ollama), you need roughly 45GB of free VRAM or unified memory. This typically means a workstation with a 48GB VRAM graphics card or a machine with 64GB of unified memory. ### Is Nemotron 70B (Ollama) good at following instructions in Atlas? Yes, Nemotron 70B (Ollama) is specifically RLHF tuned by NVIDIA for instruction adherence and helpfulness. This makes it highly reliable for following Atlas's agent instructions precisely. ### Can Nemotron 70B (Ollama) be used for code generation in Atlas? Nemotron 70B (Ollama) is a general instruct model. While capable of code generation, a dedicated 32B code specialist model might often produce higher raw diff quality for specific coding tasks, potentially with less memory. ### How large are the Nemotron 70B (Ollama) model weights? The Nemotron 70B (Ollama) model has 43GB of weights, requiring approximately 45GB of memory to serve efficiently. ### Does Atlas support local embeddings with Nemotron 70B (Ollama)? Yes, Atlas can build its code index with local Ollama embeddings. This allows you to keep your code off third-party servers, ensuring full control over your retrieval path when using Nemotron 70B (Ollama). --- Canonical HTML: https://seashell.sh/resources/models/nemotron-70b-local Source of truth: aeo_pages row `/resources/models/nemotron-70b-local` (segment: Models) (this file is generated from it, never hand-edited). Licence: SeaShell is proprietary with a free core. It is not open source and there is no public source repository.