“Speed Always Wins” is the field guide to building faster, cheaper LLMs

 Transformers scaled LLMs to jaw-dropping capabilities—but quadratic attention and ballooning KV caches are throttling real-world deployment. A new survey from Shanghai AI Lab, HKUST(GZ) and collaborators takes stock of what’s next, categorizing the ecosystem of efficient LLM architectures and where each shines. Think of it as a build sheet for teams trying to cut latency and cost without giving up quality. 

The efficiency playbook, in seven parts

• Linear sequence modeling: from linearized attention to linear RNNs and state-space models that drop KV cache and push complexity toward O(N). 

• Sparse sequence modeling: static, dynamic, and training-free sparsity to compute only the most useful token-token interactions. 

• Efficient full attention: keep softmax attention but make it practical with IO-aware, grouped, mixture, and quantized attention variants. 

• Sparse Mixture-of-Experts: routing, expert designs and MoE conversion to grow capacity without proportional FLOPs.

• Hybrid architectures: inter-layer and intra-layer mixes that blend linear blocks with full attention for a better speed/quality trade-off. 

• Diffusion LLMs: non-autoregressive generation, bridges back to AR, and early steps to extend diffusion approaches to multimodality. 

• Beyond text: how these efficiency ideas transfer to vision, audio, and multimodal stacks. 

Why this matters now

Long-context patterns—RAG, agentic tool use, deliberate reasoning, and multimodal inputs—are pushing sequence lengths and memory pressure through the roof. The survey frames these usage patterns and argues that architectural efficiency, not just better prompts or hardware, is the lever that scales the next wave of applications. 

A roadmap, not just a reading list

Beyond taxonomy, the paper stitches trends into a blueprint: pick linear/sparse methods to kill KV bloat, use efficient-full-attention where fidelity matters, layer in MoE for capacity, and consider hybrids or diffusion LLMs where generation style allows. There’s also a companion GitHub “Awesome-Efficient-Arch” list to track the space as it moves. 

If you’re building agents that browse, reason and call tools all day—or multimodal systems juggling video and audio—this survey is a timely map of the fastest lanes through today’s LLM bottlenecks.

Paper link: arXiv 2508.09834 (PDF)

OpenBMB Launches MiniCPM4: Ultra-Efficient LLMs Tailored for Edge Devices

 OpenBMB recently announced the release of MiniCPM4, a suite of lightweight yet powerful language models designed for seamless deployment on edge devices. The series includes two configurations: a 0.5-billion and an 8-billion-parameter model. By combining innovations in model design, training methodology, and inference optimization, MiniCPM4 delivers unprecedented performance for on-device applications.

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What Sets MiniCPM4 Apart

• InfLLM v2: Sparse Attention Mechanism
  Utilizes trainable sparse attention where tokens attend to fewer than 5% of others during 128 K-long sequence processing. This dramatically reduces computation without sacrificing context comprehension.

• BitCPM Quantization:
  Implements ternary quantization across model weights, achieving up to 90% reduction in bit-width and enabling storage-efficient deployment on constrained devices.

• Efficient Training Framework:
  Employs ultra-clean dataset filtering (UltraClean), instruction fine-tuning (UltraChat v2), and optimized hyperparameter tuning strategies (ModelTunnel v2), all trained on only ~8 trillion tokens.

• Optimized Inference Stack:
  Slow inference is addressed via CPM.cu—an efficient CUDA framework that integrates sparse attention, quantization, and speculative sampling. Cross-platform support is provided through ArkInfer.

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Performance Highlights

• Speed:
  On devices like the Jetson AGX Orin, the 8B MiniCPM4 model processes long text (128K tokens) up to 7× faster than competing models like Qwen3‑8B.

• Benchmark Results:
  Comprehensive evaluations show MiniCPM4 outperforming open-source peers in tasks across long-text comprehension and multi-step generation.

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Deploying MiniCPM4

• On CUDA Devices: Use the CPM.cu stack for optimized sparse attention and speculative decoding performance.

• With Transformers API: Supports Hugging Face interfacing via tensor-mode bfloat16 and trust_remote_code=True.

• Server-ready Solutions: Includes support for styles like SGLang and vLLM, enabling efficient batching and chat-style endpoints.

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Why It Matters

MiniCPM4 addresses critical industry pain points:

• Local ML Capabilities: Brings powerful LLM performance to devices without relying on cloud infrastructure.

• Performance & Efficiency Balance: Achieves desktop-grade reasoning on embedded devices thanks to sparse attention and quantization.

• Open Access: Released under Apache 2.0 with documentation, model weights, and inference tooling available via Hugging Face.

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Conclusion

MiniCPM4 marks a significant step forward in making advanced language models practical for edge environments. Its efficient attention mechanisms, model compression, and fast decoding pipeline offer developers and researchers powerful tools to embed AI capabilities directly within resource-constrained systems. For industries such as industrial IoT, robotics, and mobile assistants, MiniCPM4 opens doors to real-time, on-device intelligence without compromising performance or privacy.