DeepSeekV4SSD, an experimental app from developer yanun0323, streams routed experts from SSD to run all 284 billion parameters of DeepSeek-V4-Flash-0731 on an M-series Mac with about 30 GB of memory, …
DeepSeek released DeepSeek-V4-Flash-0731 on Hugging Face and moved its V4-Flash API to public beta on July 31, 2026, claiming major gains in agentic and coding benchmarks from re-post-training rather …
VLLM announces efficient day-0 support for Moonshot AI's Kimi K3, a 2.8-trillion-parameter Mixture-of-Experts model, achieving up to 370 tokens per second with speculative decoding on 16 NVIDIA GB300 …
DSpark introduces a sharding strategy that reduces GPU communication overhead to solve the KV cache memory bloat in long-context LLM inference, targeting latency spikes in distributed setups. The appr…
Researchers propose DeLS-Spec, a speculative decoding method that decouples long- and short-context experts to accelerate LLM inference. By adding a lightweight local head to the existing DFlash model…
DeepSeek launched DSpark, an open-source speculative decoding module that boosts AI inference speed by up to 400% on existing chips, potentially reducing demand for Nvidia's high-end accelerators. The…
Deepseek's new DSpark framework boosts per-user AI response speed by 60 to 85 percent by using a small model to propose token candidates that a larger model checks in batches, reducing dependence on U…
Chinese AI firm DeepSeek open-sourced DSpark, a speculative decoding system that accelerates large language model inference by up to 85% without altering output quality, releasing it under the MIT lic…
DeepSeek released DSpark, an open-source inference framework that accelerates AI model generation by up to 85% without hardware upgrades, challenging U.S. export controls on advanced chips. The framew…
Chinese AI startup DeepSeek unveiled DSpark, a speculative decoding framework that speeds up AI inference by up to 85% while reducing costs and chip strain. The module uses a lightweight draft model a…
An engineer analyzed speculative decoding for LLM inference acceleration, finding that while it offers lossless quality by mathematically matching autoregressive output, its speedup depends on accepta…
DeepSeek's DSpark paper reframes speculative decoding by grafting a speculative head directly onto the target model rather than training a separate draft model. The technique reduces layer duplication…
DeepSeek-AI released DeepSpec, an open-source codebase for training and evaluating draft models for speculative decoding, supporting three draft model algorithms (DSpark, DFlash, Eagle3) and requiring…
DeepSeek released DSpark on June 27, a speculative decoding framework that accelerates per-user generation speeds by 60% to 85% on its DeepSeek-V4 Flash model and 57% to 78% on the Pro variant. The fr…
DeepSeek open-sourced DSpark, a speculative decoding framework that accelerates per-user generation for DeepSeek-V4 by 57–85% over the MTP-1 baseline. The framework pairs a parallel draft backbone wit…
DeepSeek released DSpark, a speculative decoding framework now live in its DeepSeek-V4 Flash and Pro production API, delivering 51 to 400 percent throughput gains and up to 80 percent latency reductio…
DeepSeek open-sourced DSpark, a set of inference optimizations that achieve 60–85% faster generation on large language models, releasing the paper and code on GitHub.…