# Atlas with Qwen3 32B (Ollama): dense reasoning over raw speed in 2026 > Qwen3 32B (Ollama) is a fully dense 20GB model with 40K tokens (40,960) of context, Free (self-hosted), and every parameter fires on every token. Qwen3 32B (Ollama) is dense Qwen3 at 20GB. Where the 30B-A3B MoE trades memory for speed, the 32B spends every parameter on every token: slower, steadier, and generally stronger on hard single-shot reasoning. Context is 40K tokens (40,960) and pricing is Free (self-hosted). Pick Qwen3 32B in Atlas when correctness beats tokens per second, and pair it with a 1.5B tag in small_model. ## Key takeaways - Fully dense 32B: every parameter fires on every token, which tends to beat the same-size MoE on difficult one-shot problems. - 20GB download lands on a 24GB card, the same slot as Qwen2.5-Coder 32B, so you can benchmark the two head to head. - Roughly 22GB to serve, so keep num_ctx conservative on a 24GB GPU or you will spill to system RAM. - 40K tokens (40,960) of context, well under the 256K the MoE tags advertise. - Free (self-hosted), which makes exhaustive Atlas retry, re-plan, and re-diff loops economically free. - Dense inference is materially slower than the 30B-A3B MoE at the same memory cost. ## Why choose dense Qwen3 32B (Ollama) over the 30B-A3B MoE? Qwen3 32B (Ollama) is fully dense: every parameter fires on every token, which tends to beat the same-size MoE on difficult one-shot problems. Pick Qwen3 32B when correctness beats tokens per second, and accept that dense inference is materially slower at the same memory cost. Both models occupy roughly the same slot on a 24GB card, so the choice between them is not about hardware. Qwen3 32B (Ollama) spends its full compute budget on every token. The 30B-A3B routes to a subset. For an Atlas session that is one hard problem, a tricky migration, a subtle bug, a design with real constraints, the dense model is the one that gets there. For a session that is fifty easy tool calls, the dense model just makes you wait. Decide by workload, not by parameter count. ## What hardware does Qwen3 32B need to run Atlas? Qwen3 32B (Ollama) is a 20GB download and needs roughly 22GB to serve. The 20GB weights land on a 24GB card, the same slot as Qwen2.5-Coder 32B, so you can benchmark the two head to head. Keep num_ctx conservative or you will spill to system RAM. On a 24GB GPU there is roughly 2GB of headroom above Qwen3 32B (Ollama), and the KV cache lives in it. Raise num_ctx too far and the runtime spills to system RAM, which is not a graceful degradation. Because Qwen3 32B and Qwen2.5-Coder 32B are both 20GB and both land on a 24GB card, running both against the same Atlas task is a genuinely fair comparison: general dense reasoning against a code specialist, same hardware, same repo, same prompt. Pull it with `ollama pull qwen3:32b` (20GB). ## How wide is the Qwen3 32B context window? Qwen3 32B (Ollama) has 40K tokens (40,960), which is well under the 256K the MoE tags advertise. Add qwen3:32b to the ollama provider models map in atlas.json with limit.context 40960 and limit.output 8192, and let Atlas compaction handle anything longer. The window is the clearest cost of picking dense Qwen3 32B (Ollama). You trade a very large advertised context for depth on what fits. In practice 40K tokens (40,960) is a workable Atlas budget because Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, so the chunks that reach the model are the relevant ones rather than everything nearby. If your bottleneck is genuinely window size, the qwen3 MoE tags are the answer. If your bottleneck is reasoning quality inside a normal-sized prompt, Qwen3 32B is. ## Why is a free local model good for Atlas retry loops? Qwen3 32B (Ollama) is Free (self-hosted), and zero marginal cost makes exhaustive Atlas agent loops economically free. Retry, re-plan, and re-diff cycles that would show up as line items on a metered API cost only electricity on a locally served 20GB model. Agent quality often comes from iteration, and iteration is what people ration when tokens are billed. With Qwen3 32B (Ollama) there is nothing to ration. 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. Rejecting a diff and asking for another costs nothing but time. That changes how you use the agent: you stop accepting a mediocre first answer because a second one is free. ## What are the honest downsides of Qwen3 32B in Atlas? Qwen3 32B (Ollama) is materially slower than the 30B-A3B MoE at the same memory cost, and its 40K tokens (40,960) window is well under the 256K the MoE tags advertise. Both downsides are structural to dense inference, not tuning problems you can configure away. If either of those matters more than one-shot reasoning quality, Qwen3 32B (Ollama) is not your model. The mitigation for speed is not to fight it: pair Qwen3 32B with a 1.5B tag in small_model so Atlas summaries do not thrash VRAM by evicting 20GB of dense weights for a session title. The mitigation for window is compaction. Turn on local Ollama embeddings for the index, and the whole loop, retrieval included, then runs with no outbound network calls, which is the payoff for all of this local hardware. ## Setup 1. Pull the dense flagship: ollama pull qwen3:32b (20GB). 2. Plan for roughly 22GB to serve; on a 24GB GPU keep num_ctx conservative or you will spill to system RAM. 3. Add qwen3:32b to the ollama provider models map in atlas.json with limit.context 40960 and limit.output 8192. 4. Pin it with model set to ollama/qwen3:32b so the dense 32B holds the primary slot. 5. Pair it with a 1.5B tag in small_model so Atlas summaries do not thrash VRAM. 6. Turn on local Ollama embeddings for the index; the whole loop, retrieval included, then runs with no outbound network calls. ## FAQ ### qwen3 32b vs qwen3 30b-a3b for coding Qwen3 32B (Ollama) is fully dense, so every parameter fires on every token and it tends to beat the same-size MoE on difficult one-shot problems. Dense inference is materially slower than the 30B-A3B MoE at the same memory cost, so pick by workload. ### how much vram does qwen3 32b need Plan for roughly 22GB to serve Qwen3 32B (Ollama) against a 20GB download. On a 24GB GPU keep num_ctx conservative or you will spill to system RAM and lose throughput. ### what is the context window of qwen3 32b Qwen3 32B (Ollama) has 40K tokens (40,960), well under the 256K the qwen3 MoE tags advertise. Register it in the atlas.json ollama provider models map with limit.context 40960 and limit.output 8192. ### can i compare qwen3 32b and qwen2.5-coder 32b on the same gpu Yes. Both are 20GB downloads that land on a 24GB card, so you can benchmark the two head to head on the same Atlas task: Qwen3 32B (Ollama) as a general dense reasoner against Qwen2.5-Coder 32B as a code specialist. ### is qwen3 32b free to run Qwen3 32B (Ollama) is Free (self-hosted). Zero marginal cost makes exhaustive Atlas agent loops, retry, re-plan, and re-diff, economically free. The cost is the 20GB pull and roughly 22GB of memory to serve it. ### what small_model should i pair with qwen3 32b in atlas Pair Qwen3 32B (Ollama) with a 1.5B tag in small_model so Atlas summaries do not thrash VRAM. Loading a second large model for a session title would evict 20GB of dense weights from a 24GB card. ### how do i run atlas with no outbound network calls Set model to ollama/qwen3:32b, keep small_model on a local 1.5B tag, and turn on local Ollama embeddings for the codebase index. The whole loop, retrieval included, then runs with no outbound network calls. --- Canonical HTML: https://runatlas.sh/resources/models/qwen3-32b-local Source of truth: aeo_pages row `/resources/models/qwen3-32b-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.