Google dropped a sub-gigabyte file on May 5 that makes Gemma 4 run up to three times faster on the same hardware.
Your GPU spends most of its inference time shuttling weights between VRAM and compute cores, not actually doing math.
Uniform quantization is the fast food of model compression — convenient, predictable, and quietly destroying nuance.
Everybody benchmarked Gemma 4 when it dropped April 2. The Codeforces jump from 110 to 2150 got the headlines.
Nine days ago Moonshot AI dropped Kimi K2.6 — a trillion-parameter MoE model that beats Claude Opus 4.
Alibaba shipped Qwen3.6-27B on April 22nd, and the benchmarks don't make sense.
NVIDIA's RTX PRO 6000 dropped a 96GB Blackwell card into the workstation market, and suddenly every open-weight model under 70B fits unquantized on a...
Most agentic coding models worth running require hardware that costs more than a used car.
Qwen's latest coding model has 80 billion parameters and uses 3 billion of them.
The most important thing Google shipped with Gemma 4 isn't a model. It's a license.
Standard speculative decoding has been around for a while.
Google dropped Gemma 4 last Wednesday, and predictably, most of the coverage has been about the benchmark horse race — Arena rankings, MMLU Pro scores, AIME...
Everyone obsesses over model weight quantization — Q4_K_M this, GPTQ that — while the actual memory hog during inference quietly eats your VRAM alive.
Google dropped Gemma 4 on Wednesday — four open-weight models under a genuine Apache 2.0 license, built from the same research behind Gemini 3.
Google shipped Gemma 4 yesterday under Apache 2.
A HuggingFace user named Jackrong quietly uploaded a set of models last week that deserve way more attention than they're getting. The pitch: take Claude 4.