{"slug": "naive-n0-5-flash-specs-and-setup-for-the-309b-moe-coding-model", "title": "Naive-N0.5-Flash: Specs and Setup for the 309B MoE Coding Model", "summary": "NaiveAI released Naive-N0.5-Flash, an MIT-licensed open-weight mixture-of-experts coding model with 309 billion total parameters that activates 15.5 billion per token and ships with a native 1-million-token context window built without any full-attention layers. The model, built on Xiaomi's MiMo-V2.5 base model, uses 39 Sliding-Window Attention layers and 9 DeepSeek Sparse Attention layers across 48 transformer layers, requires FP8-capable NVIDIA GPUs with roughly 315 GB to hold the weights, and is priced in planned API access at $0.10/$0.40/$0.01 per million tokens for input, output, and cache reads. NaiveAI's NaiveRT inference stack claims 50 tokens/s per user in standard mode and up to 2,000 tokens/s in an Ultrafast mode using mega-kernel fusion and speculative decoding.", "body_md": "# Naive-N0.5-Flash: Specs and Setup for the 309B MoE Coding Model\n\nHow to run Naive-N0.5-Flash locally: hardware needs, FP8 quantization, context window, and setup steps for this 309B MoE coding model.\n\n## What is Naive-N0.5-Flash?\n\nNaive-N0.5-Flash is an open-weight mixture-of-experts model from NaiveAI built for coding and AI R&D work. It has 309 billion total parameters but activates only 15.5 billion per token, and it ships with a native 1-million-token context window achieved without any full-attention layers. The weights are released under the MIT license, and running it locally requires FP8-capable NVIDIA GPUs with roughly 315 GB just to hold the model.\n\n## TL;DR\n\n- **Naive-N0.5-Flash** is a 309B-parameter MoE model with only 15.5B active parameters per token, built on top of Xiaomi’s MiMo-V2.5 base model.\n- It reaches a **native 1M-token context window** using a hybrid of Sliding-Window Attention and lightweight DeepSeek Sparse Attention, with zero full-attention layers anywhere in the network.\n- Local deployment needs **FP8-capable NVIDIA hardware** , since the released weights occupy about 315 GB before accounting for KV cache and runtime overhead.\n- The model’s own inference stack, called **NaiveRT** , claims 50 tokens/s per user in standard mode and up to 2,000 tokens/s in an “Ultrafast” mode using mega-kernel fusion and speculative decoding.\n- Weights and code are **MIT-licensed** , and NaiveAI also plans API access priced at $0.10 / $0.40 / $0.01 per million tokens for input, output, and cache reads.\n- The architecture swaps out expensive global-attention layers for **DeepSeek Sparse Attention (DSA)** , which uses a 16-head indexer to pick the 2,048 most relevant tokens for each decoding step instead of scanning everything.\n- Quick-start code is available through Hugging Face Transformers (version 5.17.0+), using the `trust_remote_code` flag and an FP8 checkpoint tagged`Naive-N0.5-Flash-FP8` .\n\n### Built like a system. Not vibe-coded.\n\nRemy manages the project — every layer architected, not stitched together at the last second.\n\n## How does the hybrid SWA-DSA attention work?\n\nMost long-context models keep a handful of full-attention layers around to preserve long-range dependencies, but those layers get expensive fast as context grows, since their decoding cost scales with sequence length. Naive-N0.5-Flash removes them entirely.\n\nThe network is built from eight six-layer modules. A standard module runs five Sliding-Window Attention (SWA) layers followed by one DeepSeek Sparse Attention (DSA) layer, with the very first layer of the very first module also swapped to DSA. That works out to 39 SWA layers and 9 DSA layers across 48 total transformer layers, a roughly 5:1 ratio.\n\nSWA layers use a 128-token window, so their per-token decoding cost stays flat regardless of total context length. DSA layers handle the long-range information: a lightweight 16-head indexer scores the entire history, and the backbone then runs real attention only over the top 2,048 tokens that indexer selects. The full KV cache is still retained and the indexer still scans the whole sequence, but the expensive attention computation itself only touches a small, relevant slice of tokens. Both attention types also use sink bias, a trick that preserves attention to early tokens.\n\nOne architectural choice worth noting for anyone comparing this to DeepSeek’s original DSA work: Naive-N0.5-Flash drops DeepSeek’s MLA (multi-head latent attention) and replaces it with standard grouped-query attention using four KV groups (GQA4). The model card frames this as a simplification made during continued pretraining on top of the MiMo-V2.5 base model.\n\n## What training went into the 1M context window?\n\nGetting a model to actually use a million-token context well, rather than just accept it as an input length, usually requires dedicated training rather than a config change. Naive-N0.5-Flash went through 3.25 trillion tokens of multi-stage training specifically to adapt it to the new sparse attention structure:\n\n- 50B tokens of “Indexer Warmup,” presumably to get the DSA indexer calibrated before it’s relied on for real attention decisions.\n- 3T tokens of “Sparse Attention Training,” the bulk of the run, where the model learns to operate under the hybrid SWA-DSA regime.\n- 200B tokens of learning rate decay to finish the run.\n\nThis all happens on top of the MiMo-V2.5 base model from Xiaomi, which NaiveAI credits for providing strong foundational world knowledge and deep-research capability before the architecture swap and continued pretraining.\n\n## What hardware do you need to run it locally?\n\nThe headline number: the released weights occupy approximately 315 GB on disk. That alone rules out most consumer and even prosumer setups. The model card is explicit that you need FP8-capable NVIDIA GPUs, and that 315 GB figure is before you add memory for the KV cache, activations, and any inference-time overhead, which grows further the closer you push toward the full 1M-token context.\n\n## Remy doesn't build the plumbing. It inherits it.\n\nOther agents wire up auth, databases, models, and integrations from scratch every time you ask them to build something.\n\nRemy ships with all of it from MindStudio — so every cycle goes into the app you actually want.\n\nIn practice this points toward multi-GPU server-class hardware, the kind of setup used for other 300B+ MoE models like DeepSeek-V3 class models, rather than anything that fits on a single card. If you’re budgeting for a deployment, plan for several high-memory GPUs (think the H100/H200 class or newer) interconnected with fast links, not a single workstation card.\n\n## How do you set it up with Transformers?\n\nNaiveAI provides a quick-start path through Hugging Face Transformers. First, install a recent enough version of the library:\n\n```\npip install \"transformers[torch,kernels]>=5.17.0\"\n```\n\nThen load the FP8 checkpoint and run inference:\n\n``` python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nmodel_id = \"NaiveAI/Naive-N0.5-Flash-FP8\"\ntokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)\nmodel = AutoModelForCausalLM.from_pretrained(\n    model_id,\n    trust_remote_code=True,\n    dtype=\"auto\",\n    device_map=\"auto\",\n)\n\ninputs = tokenizer.apply_chat_template(\n    [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    add_generation_prompt=True,\n    return_dict=True,\n    return_tensors=\"pt\",\n).to(model.device)\n\noutput = model.generate(**inputs, max_new_tokens=2048)\nresponse = tokenizer.decode(\n    output[0, inputs[\"input_ids\"].shape[1]:],\n    skip_special_tokens=True,\n)\nprint(response)\n```\n\nNote the `trust_remote_code=True` flag in both the tokenizer and model calls. This is required because Naive-N0.5-Flash ships custom modeling code (`modeling_naive_n05_flash.py` and a matching configuration file) rather than relying entirely on a built-in Transformers architecture class, which makes sense given the custom hybrid attention design.\n\nFor sampling, NaiveAI recommends `temperature=1.0` and `top_p=0.95` for general use, and those are the same settings used in the model’s own benchmark evaluations.\n\n## Is it worth running locally instead of just using the API?\n\nFor most people building applications, no. NaiveAI plans to offer API access priced at $0.10 per million input tokens, $0.40 per million output tokens, and $0.01 per million tokens for cache reads, which is cheap enough that provisioning 315+ GB of FP8-capable GPU memory yourself is hard to justify unless you have specific requirements around data residency, custom fine-tuning, or air-gapped deployment.\n\nWhere local deployment makes sense is for teams that already run multi-GPU inference infrastructure for other open-weight MoE models and want to add this one to the rotation, or for researchers who want to inspect or modify the custom DSA/SWA modeling code directly. The model’s own inference system, NaiveRT, reportedly reaches up to 2,000 tokens/s per user in an “Ultrafast” mode using mega-kernel fusion, Programmatic Dependent Launch, and speculative decoding, though that level of throughput is tied to NaiveAI’s own optimized serving stack rather than a vanilla Transformers deployment, which runs closer to standard-mode speeds.\n\n## Frequently Asked Questions\n\n### How much VRAM does Naive-N0.5-Flash need?\n\nThe model weights alone occupy approximately 315 GB in FP8 format. You need additional GPU memory on top of that for the KV cache and inference overhead, which scales up as you use more of the 1M-token context window. This requires multiple FP8-capable NVIDIA GPUs, not a single consumer card.\n\n### What makes Naive-N0.5-Flash different from a standard long-context transformer?\n\nIt has no full-attention layers at all. Instead it uses a 5:1 mix of Sliding-Window Attention (fixed 128-token window, flat decoding cost) and DeepSeek Sparse Attention (an indexer selects the top 2,048 relevant tokens per step from the full history). This keeps per-token decoding cost from scaling with context length even at 1M tokens.\n\n### Is Naive-N0.5-Flash free to use?\n\nThe weights and inference code are released under the MIT license, so you can download and run them yourself at no licensing cost, though you still need to cover the GPU hardware. NaiveAI also plans a paid API at $0.10/$0.40/$0.01 per million tokens for input, output, and cache reads respectively.\n\n## Seven tools to build an app. Or just Remy.\n\nEditor, preview, AI agents, deploy — all in one tab. Nothing to install.\n\n### What base model is Naive-N0.5-Flash built on?\n\nIt’s built on MiMo-V2.5-Base, an open-weight model from Xiaomi’s MiMo team, which NaiveAI credits for strong world knowledge and deep-research foundations before applying the hybrid SWA-DSA architecture change and continued pretraining.\n\n### Does Naive-N0.5-Flash need special code to run, or does it work with standard Transformers?\n\nIt works with Hugging Face Transformers (version 5.17.0 or later) but requires `trust_remote_code=True` since it ships custom modeling and configuration files to support its hybrid attention mechanism, rather than mapping onto a pre-existing architecture class.", "url": "https://wpnews.pro/news/naive-n0-5-flash-specs-and-setup-for-the-309b-moe-coding-model", "canonical_source": "https://www.mindstudio.ai/blog/naive-n0-5-flash-local/", "published_at": "2026-10-03 00:00:00+00:00", "updated_at": "2026-10-03 18:38:52.677105+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "ai-infrastructure", "developer-tools", "ai-products"], "entities": ["NaiveAI", "Naive-N0.5-Flash", "Xiaomi", "MiMo-V2.5", "NaiveRT", "DeepSeek Sparse Attention", "Hugging Face Transformers", "NVIDIA"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/naive-n0-5-flash-specs-and-setup-for-the-309b-moe-coding-model", "markdown": "https://wpnews.pro/news/naive-n0-5-flash-specs-and-setup-for-the-309b-moe-coding-model.md", "text": "https://wpnews.pro/news/naive-n0-5-flash-specs-and-setup-for-the-309b-moe-coding-model.txt", "jsonld": "https://wpnews.pro/news/naive-n0-5-flash-specs-and-setup-for-the-309b-moe-coding-model.jsonld"}}