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aientity · one source so far

gemma4

for ml engineers, data scientists

Steadyhuggingface
Signal score
0
as of 16d ago
Live items
1
Trajectory
⏳ Too early
needs a few more snapshots

Why this scored 0

every term, weighted
Velocity+0.0 / 40

Its fastest-moving item, measured against the pace of its own source

Acceleration+11.8 / 25

Whether that velocity is itself speeding up, as a per-hour rate

Cross-source spread+0.0 / 25

How many independent communities its own items come from

Recency+0.0 / 10

Decays to zero over 14 days, counted from when we first saw it

Saturation penalty−15.0 / 30

Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it

Composite-3.3 → clamped to 0

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

Signal history

7-day window (free)
Not enough history yet — the trend line fills in as snapshots accrue (every 30 min).

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.

  1. 1
    24

    Gemma 4 at Over 70 Tokens/s on a 2021 Laptop's 4 GB GPU: The Live Demo, Step by Step

    Gemma 4 runs at 70 tokens/s on a 4 GB laptop GPU, enabling local inference. · for ml engineers

    devtoCooling 2 · llamacppai24d ago
  2. 2
    24

    Serving Gemma4 with Rust on vLLM 🦀

    Running Gemma4 on vLLM with Rust shows high-performance inference on AWS GPU instances. · for ML engineers

    devtoCooling 2 · vllmai72d ago
  3. 3
    22

    Gemma 4 on an Amazon SageMaker Endpoint: AWS CLI, NVIDIA L4, and an MCP Server

    Shows how to deploy Gemma 4 on a SageMaker endpoint via AWS CLI and L4 GPU. · for ML ops

    devtoSteadyai30d ago
  4. 4
    22

    Three Gemma 4 Deployments on One T4G for Under $3: What the Runtime Changes, and What It Doesn't

    Shows cost-effective Gemma 4 inference on a single AWS T4g instance for ML workloads. · for ml engineers, cloud architects

    devtoSteadyai54d ago
  5. 5
    14

    Five Gemma-4 models, one accelerator: what porting E2B 31B to AWS Inferentia2 taught me

    gemma-4 models · for ai engineers

    devtoCooling 2 · awsai101d ago
  6. 6
    13

    autotrust/GEV-26B-Decide-NVFP4 (text-classification)

    Introduces a new transformer model for text-classification and image-text-to-text tasks. · for ml engineers, data scientists

    huggingfaceSteadyai−5 saturated18d ago
  7. 7
    12

    Gemma 4 Inference on AWS: Bedrock, SageMaker, GPUs, Inferentia and Trainium Behind One Strands Agent

    Gemma 4 runs on AWS via Bedrock, SageMaker, GPUs, Inferentia and Trainium using Strands Agent. · for ML engineers, cloud architects

    devtoCoolingai19d ago
  8. 8
    11

    Gemma 4 on Amazon SageMaker: QAT Weights Decode 2.05x Faster Than bf16 on One L4

    Gemma 4 quantized runs 2.05× faster on SageMaker L4, lowering inference latency and cost. · for ML engineers

    devtoCoolingai30d ago
  9. 9
    10

    Porting Gemma-4 12B (the encoder-free multimodal one) to AWS Inferentia2

    Gemma-4 ported to AWS · for ai researchers

    devtoSteadyai101d ago
  10. 10
    8

    Porting Gemma-4 (2B / 4B / 12B) to AWS Inferentia2

    Gemma-4 porting · for ai researchers

    devtoSteadyai105d ago
  11. 11
    7

    26B Gemma 4 QAT Deployment with GCE g2-standard, NVIDIA L4, MCP, and Antigravity CLI

    Gemma 4 deployment · for ai founders

    devtoSteadyai102d ago
  12. 12
    7

    Gemma4 DevOps In Action

    DevOps in action · for devops teams

    devtoSteadydevtools98d ago