Gemma 4 on a Tesla T4: QAT Weights Decode 1.79x Faster Than bf16

DEV Community·7 points·xbill·12 days ago·dev.to

A step by step deployment of Gemma 4 E2B with vLLM on a single Tesla T4 attached to a Compute Engine VM, and a measured comparison of the QAT w4a16 checkpoint against the bf16 reference on the same card.

Read the full article at dev.to →

#gemma #qat #tesla #bf16 #vllm #decode #79x #weights

Related stories

Gemma 4 on Amazon SageMaker: QAT Weights Decode 2.05x Faster Than bf16 on One L4dev.to
DEV Community · 0 points · 5 days ago
Gemma 4 on Amazon SageMaker: QAT Weights Decode 2.05x Faster Than bf16 on One L4dev.to
DEV Community · 7 points · 5 days ago
Gemma 4 on a Tesla T4, Part 3: Int4 Embeddings Serve E2B in 2.86 GiB at 2.30x bf16dev.to
DEV Community · 8 points · about 13 hours ago
Gemma 4 on a Tesla T4, Part 2: The Minimum GCE VM and a Script to Drive Itdev.to
DEV Community · 13 points · 8 days ago
Tesla workers balk at training Optimus humanoid robots as replacementsarstechnica.com
Ars Technica · 0 points · 5 days ago
Gemma 4 on an 2021 4 GB Laptop GPU: QAT Takes It From 9.5 GiB to 1.6dev.to
DEV Community · 10 points · 20 days ago

Related topics

sagemaker8 vllm16 amazon42 nvidia57 decode6 mcp92 jev349 qat4 bf165 aws42 weights12 server92