tpu
for ML engineers, research engineers
Why this scored 5
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology
Outlook
low confidence · estimate, not a guarantee7-day
~13
range 0–25
14-day
~14
range 0–29
30-day
~15
range 0–34
Signal history
7-day window (free)projected trajectory (estimate, not a guarantee)
The evidence
The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.
- 162
Google's QAT Gemma 4 26B-A4B on One TPU v6e: 15.6x the KV Cache and 1.9x the Throughput of FP8
Gemma 4 26B runs 15.6× KV cache speed on one TPU v6e, improving inference throughput. · for ML engineers
devtoEmergingai29d ago - 230
Self-hosting a lite agent backend on one TPU: Gemma 4 E2B + vLLM on a v5e-1
Run lite agent backend on one TPU · for devtools founders
devtoSteady 2 · vllmhardware77d ago - 328
Deploying a QAT Checkpoint Your Serving Stack Can't Load: Gemma 4 E2B in Pure JAX on One TPU
Runs quantized Gemma-4 E2B on JAX using a single TPU, enabling efficient LLM serving. · for ml engineers
devtoSteady 2 · jaxai68d ago - 422
Serving Gemma 4 E2B on a TPU v6e-1: what Trillium buys, and what it doesn't
Trillium buys TPU · for ai founders
devtoSteadyai76d ago - 522
Inference on Google Cloud: Precision, Memory and Capacity on TPU, from v5e to Ironwood
New TPU v5e to Ironwood improve inference precision, memory and capacity on Google Cloud. · for ml engineers, cloud architects
devtoSteadyai17h ago - 618
PyTorch on TPU: Hands On With Colab
Demonstrates running PyTorch models on TPUs via Colab for faster training. · for ML engineers, research engineers
devtoSteadyai2d ago - 718
TPU Deployments with Gemma 31B, v6e-8, and Antigravity CLI
TPU deployments with Gemma · for ai engineers
devtoCooling 2 · gemmaai102d ago - 818
Gemma 4 E2B in Pure JAX on a Colab TPU: Google's 4-Bit Export Against an Exact Repack
Shows a 4-bit Gemma model running on JAX on a Colab TPU for efficient inference. · for ml engineers, researchers
devtoSteadyai16d ago - 917
Small Apache-2.0 Models Against Gemma 4 E2B on One TPU v5e: Which Ones Keep Up?
Compares open-source models to Gemma on a single TPU v5e. · for ml engineers
devtoSteadyai13d ago - 1015
Serving Gemma 4 2B on a Single TPU v5e Chip with MCP and Antigravity CLI
Gemma 4 optimized on TPU · for ai engineers
devtoSteadyai82d ago - 1113
Gemma 4 E2B on a Single TPU v6e Chip: A Serving Deep Dive
Gemma 4 E2B on TPU v6e · for ai researchers
devtoSteady 3 · gemmaai97d ago - 1213
Self-Hosted Gemma 4 on TPU v6e: Deployment & SRE with Antigravity
Gemma deployment on TPU · for ai devs
devtoSteady 2 · gemmaai92d ago - 1312
One TPU Chip, Eight Agents: Serving Small Agent Workloads with Raw JAX
Efficient agent workloads · for ai engineers
devtoSteady 2 · jaxai88d ago