gemma

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

Gemma 4 on an 2021 4 GB Laptop GPU: QAT Takes It From 9.5 GiB to 1.6

Quantization-aware training shrinks Gemma 4 memory to run on 4 GB laptops. · for ml engineers

Jev After Eight Days of Independent Tests: Level With Mid-Price LLMs, Behind the Frontier

Jev matches mid-price LLMs but trails leading models, indicating competitive performance limits. · for ml engineers, ai researchers

I sleep 4 hours a night because of email, so I built a sloth that reads it for me (Gemma 4 on an Arduino UNO Q)

Arduino-based email reader demonstrates low-power automation for personal workflows. · for embedded developers

Should I Go Out? A Local Gemma Agent That Picks One Outdoor Plan, Then Tells You to Put Your Phone Away

AI agent curates outdoor plans and enforces phone-free time for users. · for mobile app developers, UX designers

Running a Jev-Style Decision Model on One TPU v6e: What Fits, What It Costs, and What Changes From a GPU

Runs Gemma model on a TPU v6e, comparing cost and performance to GPU. · for ml engineers

Gemma 4 on Amazon SageMaker: 4-Bit Embeddings Decode up to 1.39x Faster on One L4

Gemma 4 on SageMaker runs 4-bit embeddings 1.39× faster on an L4 instance. · for ml engineers

Gemma 4's MTP Drafter, Explained: Speculative Decoding and a 4-Bit Drafter at 60% of the Size

Speculative decoding lets a 4-bit drafter run at 60% size, lowering inference costs. · for ml engineers, ai developers

Qwen and Gemma With Thinking On: Thinking Pays Qwen on Tool Calls, Gemma Still Leads

Qwen adds tool-call support while Gemma remains top performer. · for ml engineers, ai researchers

Do Drafters Work on Small Models? Gemma 4 E2B and E4B on Two Small GPUs

Benchmarks show Gemma models run on small GPUs, guiding low-cost deployment decisions. · for ml engineers, platform teams

Comparing Qwen and Gemma on the Low End: Qwen Is Faster Without Drafting, Gemma Has Better Quality

Low-end tests show qwen runs faster while gemma yields higher quality outputs. · for ml engineers, ai developers

A 4 GB Laptop GPU vs a 6-Core CPU on Gemma 4, Re-Measured in ABBA Order: 4.1x

Benchmark compares 4 GB GPU vs 6-core CPU on Gemma 4 using llama.cpp and CUDA. · for ml engineers, performance testers

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

How I Built Parchi: Turning Messy WhatsApp Orders into a Clean Order Sheet with Gemma

Gemma was used to turn chaotic WhatsApp orders into a clean order sheet for Parchi. · for SaaS founders, ops engineers

Can a QAT 12B Fit an 8 GB GPU? Gemma 4 12B on an RTX 3050

Shows 12B model can run on 8 GB GPU, guiding resource planning for ML workloads. · for ml engineers, devops

Gemma 4 on a Tesla T4, Part 3: Int4 Embeddings Serve E2B in 2.86 GiB at 2.30x bf16

Gemma 4 on T4 delivers int4 embeddings at 2.30× bf16 speed in 2.86 GiB. · for ml engineers

Gemma 4 on Amazon SageMaker: The NVIDIA T4 Decodes at 0.8x of the L4 With the Same Answers

Gemma 4 runs on SageMaker using NVIDIA T4 with near-L4 answer quality. · for ML engineers, cloud architects

Gemma 4 on Amazon SageMaker: The NVIDIA T4 Decodes at 0.8x of the L4 With the Same Answers

Gemma 4 runs on SageMaker using NVIDIA T4 with near-L4 answer quality. · for ML engineers, cloud architects

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

Two Rust Clients for Gemma 4: Calling the Endpoint vs. Calling the MCP Server 🦀

Rust devs can choose endpoint or MCP server to integrate Gemma 4 models. · for rust developers