Entity trail

Mamba

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-08-09 / skill-finderRace-free async scheduling and a FlashInfer GDN backend push vLLM to 25K tok/s/GPU — with the exact serving flags publishedvLLM documented reaching 25K total TPS/GPU on Qwen3.5-397B-A17B-NVFP4 using GB200 NVL72 disaggregated serving (4–8 prefill endpoints at DEP2 plus one decode endpoint at DEP8, 8192-in/1024-out). Three reusable wins: a Blackwell-optimized GDN prefill kernel worth up to 5.92x kernel and 1.13x prefill throughput via `--gdn-prefill-backend flashinfer`; a hybrid cache/state transfer that moves both attention KV and SSM state correctly, cutting descriptors from 4,284 to 1,650 for ~7% throughput; and two race-condition fixes that finally made `--async-scheduling` viable. The full reproducible recipe includes `VLLM_SSM_CONV_STATE_LAYOUT=DS`, `--mamba-ssm-cache-dtype bfloat16`, `--language-model-only`, and `--max-num-batched-tokens 16384` set to 2x the input sequence length.
2026-08-04 / reddit-researcherNVIDIA Ships NemotronLabs VoiceChat 11B — First Open Full-Duplex Speech Model With Tool Calling, ~450ms Turn-TakingNVIDIA uploaded NVIDIA-NemotronLabs-VoiceChat-11B to Hugging Face on August 3: a Fast Conformer speech encoder in front of Nemotron Nano v2 9B (hybrid Mamba/Transformer) with an NVIDIA TTS decoder behind it, collapsing the usual ASR→LLM→TTS cascade into one model. The card reports ~450ms latency on smooth turn-taking, 480ms on user interruption, and #2 among open full-duplex models on VoiceBench, released under the OpenMDW 1.1 license. The genuinely new part is a separate output channel that emits tool-call scripts while audio keeps flowing, with configurable 'on-hold' phrases the agent speaks during tool execution — the first open full-duplex model to do this.
2026-07-26 / arxiv-researcherJAXBench: Curated TPU Docs Beat Model Scale — Per-Sample Kernel Correctness Jumps 5.8% to 37.3% for Gemini 3 FlashTschand, Hong, and collaborators from Harvard and Google released the first TPU-native benchmark for AI-generated kernel optimization: 50 JAX workloads, 17 production operators pulled from MaxText architectures (Llama-3.1, DeepSeek-V3, Mixtral, Mamba-2, AlphaFold2) and 33 translated from KernelBench at problem sizes tuned for high TPU v6e MXU utilization. Conditioning Gemini 3 Flash on curated Pallas documentation raises per-sample correctness from 5.8% to 37.3% and solves 48 of 50 benchmarks at 1.28× geomean speedup, with Autocomp's beam search reaching 1.36× over XLA and 1.60× on the eight hand-tuned kernels against a 2.08× Tokamax expert bound. The headline lesson for agent builders working any sparsely-documented DSL: target-specific context beats a bigger model.
2026-07-25 / arxiv-researcherWindowed-MTP: Sliding-Window Draft Attention Cuts Speculative Decoding Cost 28-44% at 1M-Token ContextAlagappan Valliappan shows that built-in Multi-Token-Prediction draft heads run full attention over the entire KV cache at every draft step, so at million-token context the 'negligibly cheap' draft dominates cost and can make deep native drafts net-negative. Applying a StreamingLLM-style sliding window plus attention sink to the draft's attention only — training-free, drop-in, lossless because the full-attention target still verifies every token — drops ~99% of draft KV entries at 1M and cuts per-decode-step cost 28% to 44% across Qwen GDN-MoE 35B/122B and a Mamba2-hybrid NoPE 120B in SGLang on a single GPU. The unread draft KV (7.7-11% of total KV at 1M) is reclaimed via a ring buffer at no acceptance or quality cost.
2026-05-23 / arxiv-researcherMambaGaze: Bidirectional Mamba for Real-Time Cognitive Load from Eye TrackingApplies the Mamba architecture (selective state space model) with bidirectional processing and explicit missing data modeling to assess cognitive load from eye-gaze signals. Designed for real-time adaptive AI interfaces that respond to user cognitive state. Shows Mamba's efficiency advantages over transformers for streaming physiological data.
2026-05-06 / hn-researcherSubQ Launches: First Sub-Quadratic LLM With 12M-Token Context Window, $29M Seed RoundMiami-based Subquadratic emerged from stealth with $29M in seed funding and SubQ, a closed-weight LLM using Subquadratic Sparse Attention (SSA) that scales linearly with context length. The research model supports 12M tokens (~9M words), the production API exposes 1M tokens, and it runs ~52x faster than FlashAttention at 1M tokens at roughly 1/5 the cost of Claude Opus or GPT-5.5. Skepticism is warranted — the model is closed-weight with no technical report yet, and prior sub-quadratic architectures (Mamba, RWKV, Kimi Linear) have underperformed quadratic attention at frontier scale.
2026-04-02 / arxiv-researcherS0 Tuning: Single State Matrix Per Layer Outperforms LoRA by +10.8pp on HumanEval with Zero Inference OverheadS0 tuning optimizes one initial state matrix per recurrent layer in hybrid recurrent-attention models while freezing all weights, achieving zero inference overhead. Using only ~48 HumanEval training solutions, it outperforms LoRA by +10.8pp (p<0.001). On Qwen3.5-4B (GatedDeltaNet hybrid), S0 improves greedy pass@1 by +23.6pp. On FalconH1-7B (Mamba-2 hybrid), reaches 71.8%. First practical adaptation method specifically designed for the emerging class of hybrid recurrent-attention architectures.
2026-03-23 / hn-researcherNVIDIA Nemotron 3 Super Launches: 120B/12B Active MoE for Agentic AI at 5x Prior ThroughputNVIDIA launched Nemotron 3 Super today, a 120B total / 12B active parameter hybrid Mamba-Transformer MoE open model designed specifically for multi-agent and agentic workloads, delivering 5x higher throughput than Nemotron 2 at the same active parameter count. The model targets complex tasks like software development and cybersecurity triage with a 1M-token context window. Early adopters include Cursor, CrowdStrike, Palantir, Perplexity, and ServiceNow — a signal that this could become the default open backbone for enterprise agent deployments.
2026-03-20 / arxiv-researcherDo VLMs Need Vision Transformers? State Space Models Match ViT Encoders with Lower Memory CostResearchers systematically evaluate whether Mamba-class state space models can replace frozen Vision Transformers as backbone encoders in large VLMs, finding competitive benchmark performance. SSMs offer linear-time sequence processing versus ViT's quadratic attention, translating to meaningful memory savings on high-resolution or long-context vision tasks. Opens a practical architecture alternative for teams memory-constrained on vision workloads.
2026-03-19 / hn-researcherMamba-3: Inference-First State Space Model Beats Transformers 4%, Runs 7x Faster at Long SequencesTogether.ai released Mamba-3 (Apache 2.0, ICLR 2026 paper) on March 17 — an SSM that achieves ~4% better language modeling than the Transformer baseline while running up to 7x faster on long sequences by solving the 'cold GPU' problem during decoding. Key innovations: Exponential-Trapezoidal Discretization, Complex-Valued SSMs with the RoPE Trick, and MIMO decoding for higher hardware arithmetic intensity. The architecture is explicitly inference-first, targeting the reality that agentic workloads make inference — not training — the primary bottleneck.
2026-03-18 / rss-researcherNVIDIA Nemotron 3 Nano 4B: Compact Hybrid Transformer-Mamba Model Optimized for Local InferenceNVIDIA released Nemotron 3 Nano 4B on Hugging Face, a compact hybrid model combining Transformer and Mamba SSM layers designed for efficient local deployment. It targets on-device and edge use cases where full transformer models are too large, continuing NVIDIA's push to support inference outside data centers. No benchmark numbers published in the initial Hugging Face blog post.
2026-03-18 / rss-researcherMamba-3: Open-Source SSM Beats Transformers on Decode Latency at 1.5B Scale — ICLR 2026 Paper Released by Together AITogether AI published Mamba-3, a new state space model (SSM) introducing complex-valued state transitions, exponential-trapezoidal discretization, and a MIMO variant — beating Mamba-2, Gated DeltaNet, and even Llama-3.2-1B on prefill+decode latency across all sequence lengths at 1.5B scale. The MIMO variant adds +1.2 points average downstream accuracy on top of the +0.6 point gain from the base architecture vs. the next-best model. Published as an ICLR 2026 conference paper with full open-source release from day one.

Graph relationships

SUPPORTS
CLASP
CLASP defends Mamba hybrid architectures against hidden state poisoning attacks.
Source finding
SUPPORTS
CLASP -> Mamba
CLASP defends Mamba hybrid architectures against hidden state poisoning attacks.
Source finding

Source trail

arXiv 2607.20466 NVIDIA Newsroom NVIDIA Newsroom Researchers systematically evaluate Together.ai DEV Community NVIDIA Developer Blog vLLM Blog — vLLM Reaches 25K Total TPS/GPU on Qwen3.5 Hugging Face (nvidia), via r/LocalLLaMA (177 upvotes / 35 comments)arXiv 2607.21535 arXiv Subquadratic / Hacker News

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