ResearchAudio

Daily index / open-model discovery

See what is moving before you download the weights.

Twelve trending Hugging Face text-generation models, translated into INT4, INT8, and BF16 weight-plus-headroom floors. The source refreshes daily; the assumptions stay visible.

Inspect today’s hardware index → Use the public JSON snapshot → Inspect another Hugging Face repository →

Three boundaries

Trend is not quality.
Hub attention does not prove benchmark strength, safety, or production readiness.

Active is not resident.
MoE active parameters can be much smaller than the total weights that need memory or offload.

Fit is not speed.
A weight floor says nothing about tokens per second, latency, context, or runtime support.

Reproducible method

What the daily refresh does—and refuses to guess.

Discovery. The refresh requests public text-generation repositories from the Hugging Face Hub models API, sorted by trending score. It keeps the first twelve repositories with a safetensors total between one billion and two trillion parameters.

Weight floor. The calculation is parameters × bits ÷ 8 ÷ 1024³. The displayed value then adds 20% headroom as a consistent first-pass planning allowance. It does not claim that every real four-bit format stores exactly four bits per parameter.

What is missing. The table deliberately excludes KV cache, activations, CUDA or Metal workspace, graph capture, allocator fragmentation, metadata, system memory, offload, multi-GPU communication, prompt processing, decode speed, and power.

Failure behavior. A refresh must return at least eight valid models and pass the full site verifier before it can be committed. If the upstream API fails or the response becomes invalid, the workflow fails and the last verified snapshot remains public.

Machine-readable evidence. The same validated snapshot is available as public JSON with source metadata, formulas, exclusions, timestamps, and every displayed number.

Turn discovery into a deployment check

A trending model is the start of the investigation.

Carry the parameter count into an editable plan, then replace generic precision and headroom with the exact checkpoint, architecture, context, runtime, and hardware.

Weights + cache + GPU count

Build the full memory plan

Add exact artifact size, layers, KV heads, head dimension, context, concurrency, runtime headroom, and usable VRAM.

Open the GPU calculator → Start from hardware

Find what your GPU can hold

Use the VRAM you already own to find the largest standard weight tier below a conservative capacity ceiling.

Open the model finder → Context + concurrency

Expose the KV-cache bill

Calculate cache memory per token, sequence, and simultaneous request from the actual model architecture.

Open the cache calculator → Hardware decision system

Follow the complete evidence path

Connect model size, checkpoint, quantization, context, runtime, GPU tier, and exact released-model guides.

Open the hardware guide →

Trending model FAQ

Use the signal without confusing it for proof.

The index answers which text-generation repositories are moving on the Hub and what their total-parameter arithmetic implies. It cannot choose a model for your task.

Review the official Hugging Face models API → Review the safetensors format →

How much VRAM does a trending LLM need?

Start with total parameters multiplied by bits per parameter, converted to binary GiB, then add cache, runtime workspace, and measured headroom. This index shows only a weight-plus-20%-headroom floor.

Does an MoE active parameter count determine VRAM?

No. Active parameters can describe compute per token, while resident weights still need storage or an explicit offload path. The index uses the Hub safetensors total parameter count.

Is Hugging Face trending score a benchmark?

No. It is a discovery signal. It does not prove model quality, safety, speed, licensing suitability, or production readiness.

The model queue changes every day

Get the evidence behind the next model release.

ResearchAudio turns model announcements, checkpoint claims, memory boundaries, and serving constraints into practical decisions for engineers and builders.