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.