Modular: Day Zero: MiniMax M3 Open Weights on Modular Cloud

MiniMax released the open-weights MiniMax M3 model on Modular Cloud, featuring a new Sparse Attention operation that achieves up to 15.6x speedup on decode while maintaining a 1 million token context window. The model is optimized for coding, agentic tasks, and native multimodality, with Modular providing deployment options on its cloud or in customer VPCs.

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Careers 👋 We’re currently hiring Culture What we believe Contact Us Request a demo June 12, 2026 Modular Team Company MiniMax M3 is the newest open-weights model that has been optimized for coding, agentic work, and native multimodality for MiniMax. A few things that make this a frontier model are: Behind M3 is a new MiniMax Sparse Attention MSA operation. MSA is what enables a 1M context to be served, and a big part of what makes M3 demanding to run well. But, if optimized, MSA’s design allows it to cut the per-token attention compute to roughly 1/20th of its full-attention predecessor. This results in around 9.7× speedup on prefill and 15.6× speedup on decode, while matching full attention across the vast majority of workloads. MSA splits every attention layer into two parts: which KV to look at, and how to attend to it. The first is solved by introducing an indexing layer. For each query, the indexer scores candidate KV blocks and chooses the top-k blocks. The indexer also maintains a cache of index keys with a single shared head and a small head dimension. By focusing only on top scoring KV cache blocks, MSA only computes the attention of the relevant 128 tokens in the KV caches rather than the full block. One MSA layer, conceptually s = Q idx @ K idx.T idx scale single shared index head, tiny d idx -- nearly free S = block max pool s, B=128 token scores - 128-token block scores S :, :init blocks = INF force-select the attention-sink blocks S :, local window: = INF - eps force-select the recent window I = topk per kv group S, k ONE selection, shared by every head in the GQA group O = softmax attention Q, K I , V I ordinary GQA over the REAL K/V of the selected blocks The model produces selection in query-major form: for each query, a list of top-k block IDs. The natural kernel follows that shape — loop over queries, gather their selected KV blocks, and then attend. Executing in query-major order would mean each query independently gathers its selected blocks, the same KV block may be fetched from HBM many times which is not very efficient . Query-major: the natural schedule for q tile in queries: parallel across threadblocks for blk in I q tile : this tile's top-k blocks K blk, V blk = load block blk hot blocks re-fetched by EVERY threadblock that picked them online softmax update q tile, K blk, V blk To avoid the repeated loads, MSA inverts the mapping by grouping the queries by the KV block they selected; i.e. executing in key-block-major form and what MiniMax calls “KV outer gather Q”. As a result, we can improve the arithmetic intensity since the blocks are loaded once, before computing partial attention for all of those queries, and then merging the partial results. Once per step: transpose the selection a sparse-matrix transpose into CSR k2q = invert I row = seq, kv block ; entries = queries that selected it work = chunk rows k2q, q budget split hot rows for load balance more below Block-major forward: one threadblock per work item -- each KV byte leaves HBM once blk, q list = work work id K blk, V blk = bulk load blk ONE contiguous load; resident for the threadblock's lifetime for q tile in tiles q list, BM : stream the selecting queries through it Q t = gather rows Q, q tile gather the queries scattered rows O p, lse = attend one block Q t, K blk, V blk single-tile softmax -- next section O partial q tile, slot q tile, blk = O p per- query, block partials, LSE partial q tile, slot q tile, blk = lse merged by a separate combine pass This structure has an added benefit of simplifying the online softmax computation. Remember that in query-major attention one needs to perform online softmax. But in the block-major format, a thread block only ever sees one KV block per query group. Thus the softmax can be performed on a single tile without the need for an online correction. This is very much similar to the split-kv reduction step in flash decoding. The MiniMax M3 model bring novel innovations that require whole stack optimizations - from kernels to cloud. This is only possible in the Modular platform. MiniMax M3 is available on Modular Cloud today for enterprise customers. Talk to our AI engineers to request access today. Discover what Modular can do for you Hippocratic AI partners with Modular to power flexible, high-quality inference for real-time patient conversations May 18, 2026 Modular Opens Edinburgh & San Francisco Offices April 10, 2026 Modverse 54: From GTC to Edinburgh, a Community Building Momentum March 31, 2026 Build the future of AI with Modular Sign up today Signup to our Cloud Platform today to get started easily. 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