Topic

#edge-inference

Loot, blog posts and adjacent themes connected to this topic. Follow the tag to keep it in your orbit.

#edge-inference
1Shown loot
0Shown articles
8Linked neighbor tags
Topic paths

If you want to go deeper, the adjacent tags are the fastest way to compare and branch into related workflows.

#ai-generated#automated#llama.cpp#mlx-vlm#open-weight#sglang#speculative-decoding#vision-language
Loot

More from this topic

Explore all loot
Speed up an open vision model on edge hardware with LFM2.5-VL-DSpark

Speed up an open vision model on edge hardware with LFM2.5-VL-DSpark

0
Text: AI-generated
#ai-generated#automated+7
AI-generated · Automatically published by LinkLoot. Liquid AI's experimental LFM2.5-VL-DSpark drafter accelerates LFM2.5-VL-3B decoding while keeping the target model's output quality unchanged. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. Liquid AI's LFM2.5-VL-DSpark is an open-weight speculative-decoding drafter for the LFM2.5-VL-3B vision-language model. It adds a small side model instead of changing the target model, so the target verifies every proposed token. What it provides Up to 3.13× faster decoding on an edge device and up to 2.66× on an H100 in Liquid AI's reported tests. End-to-end gains up to 2.62× on the tested M5 Max setup and 2.27× on the tested H100 setup. A roughly 280M-parameter drafter, adding about 8.9% to the deployed parameter count. Day-one support for llama.cpp, MLX-VLM, and SGLang, with Safetensors and GGUF checkpoints available through Hugging Face. Who should look at it This is useful for developers serving a small open vision-language model on Apple Silicon, local edge hardware, or a single GPU where decode time matters. The reported measurements use FP16 or BF16 and batch size 1; Liquid AI says quantized-model acceleration is outside this release's scope, so treat the published multipliers as hardware- and workload-specific rather than a universal benchmark. Start with the model card and the runtime-specific commands, then benchmark your own image mix and output lengths against the plain LFM2.5-VL-3B target. Sources Liquid AI announcement Hugging Face release write-up and model artifacts
Free
Review open
0
Open loot
Blog

Related reads

Browse blog
No blog posts for #edge-inference yet

There is no published article with this tag right now. Browse the blog for adjacent themes or follow the tag for future updates.