DE
deepset
Language model · Germany ·Oct 2020

GBERT-Large

Language Open (Restricted)
335M
Parameters

About GBERT-Large

GBERT-Large is an AI model developed by deepset, in the language category, released in 2020, made available as an open-weights model with 0.3B params.

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

→ More models by deepset → All language models → AI benchmarks leaderboard

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Sized to what GBERT-Large 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 GBERT-Large? ▶

GBERT-Large is an AI model developed by deepset, in the language category, released in 2020. It is tracked on GenAIList with its specifications, benchmark scores and provider pricing.

Who created GBERT-Large? ▶

GBERT-Large was developed by deepset and released in 2020.

Is GBERT-Large open source or proprietary? ▶

GBERT-Large 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 GBERT-Large cost? ▶

Pricing for GBERT-Large depends on the provider. See the providers table on this page for the latest API rates.

How does GBERT-Large perform on benchmarks? ▶

Benchmark scores for GBERT-Large 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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