Edge Inference Benchmarks
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On-Device benchmarks across devices and models.
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WilhelmT
updated a
Space about 1 hour ago
vllm serve
4
#2 opened 5 days ago
by 
ParisXu
JonnaMat
updated a
Space 4 days ago
Post
1210
⚡ FlashHead: Fast LM Head Inference - Now a Simple vLLM Plugin
flash-head replaces the dense LM head with a two-stage retrieval pipeline - up to 2x inference speedup, training-free. Previously required custom Docker images; now it's just:
✨ The plugin activates automatically via vLLM's
🧩 Supported models (full collection):
Qwen Qwen3, 
meta-llama Llama3, 
google Gemma3, 
nvidia Cosmos-Reason2 - BF16 and W4A16 variants.
https://huggingface.co/collections/embedl/flashhead
📊 embedl/Edge-Inference-Benchmarks
🔧 Benchmark it yourself:
FlashHead shines at low batch sizes; the typical real-time / on-device use case. 🚀
flash-head replaces the dense LM head with a two-stage retrieval pipeline - up to 2x inference speedup, training-free. Previously required custom Docker images; now it's just:
pip install flash-head
vllm serve embedl/Qwen3-1.7B-FlashHead-W4A16✨ The plugin activates automatically via vLLM's
vllm.general_plugins entry point. No source patches, no custom imports.🧩 Supported models (full collection):
https://huggingface.co/collections/embedl/flashhead
📊 embedl/Edge-Inference-Benchmarks
🔧 Benchmark it yourself:
vllm bench latency --model embedl/Qwen3-1.7B-FlashHead-W4A16 --batch-size 1
# Baseline comparison
FLASHHEAD_ENABLED=0 vllm bench latency --model embedl/Qwen3-1.7B-FlashHead-W4A16 --batch-size 1FlashHead shines at low batch sizes; the typical real-time / on-device use case. 🚀
- 2 replies
JonnaMat
updated a
collection 4 days ago
JonnaMat
updated 4
models 4 days ago