AI
Allen Institute for AI
Language model · United States ·Mar 2019

SciBERT

Language Open (Restricted)
110M
Parameters

About SciBERT

SciBERT is an AI model developed by Allen Institute for AI, in the language category, released in 2019, made available as an open-weights model with 0.1B params.

On this page you'll find SciBERT's full specifications. Review provider pricing and benchmark scores below, or compare SciBERT head-to-head with other language models.

→ More models by Allen Institute for AI → All language models → AI benchmarks leaderboard

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Sized to what SciBERT actually needs (~8k context at Q4), not to the biggest GPU: Good = cheapest that runs it, Better = best value, Best = most headroom. Speed is a hardware estimate; anything past ~120 tok/s is shown as instant.

Best
Intel Arc B580 12GB
Consumer GPU · ~$249
~120 tok/s
Q4 · fits full context · up to 8k ctx
Runs with: ExLlamaV2, KoboldCpp, LM Studio
See full estimate →

Software support

·ExLlamaV2 ·KoboldCpp ·LM Studio ·MLX ·Ollama ·SGLang ·TensorRT-LLM ·llama.cpp ·vLLM ?KTransformers ?text-generation-webui

✓ measured · · compatible · — not supported. Informational only — speed is hardware-based.

Consumer GPU
120+ tok/s
33 options →
Apple
120+ tok/s
18 options →
Datacenter GPU
120+ tok/s
13 options →

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Frequently asked questions

What is SciBERT? ▶

SciBERT is an AI model developed by Allen Institute for AI, in the language category, released in 2019. It is tracked on GenAIList with its specifications, benchmark scores and provider pricing.

Who created SciBERT? ▶

SciBERT was developed by Allen Institute for AI and released in 2019.

Is SciBERT open source or proprietary? ▶

SciBERT ships as an open-weights model: you can download and self-host the weights, though the licence may place some restrictions on use.

How much does SciBERT cost? ▶

Pricing for SciBERT depends on the provider. See the providers table on this page for the latest API rates.

How does SciBERT perform on benchmarks? ▶

Benchmark scores for SciBERT are listed on this page as they are published. You can compare it head-to-head with other models on the GenAIList compare tool.

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