Alibaba NLP · GTE

GTE Large EN v1.5 GPTQ

Name: Serve GTE Large EN v1.5 GPTQ on Badgr
Brand: Badgr
Availability: InStock

Unverified · community-discovered, not yet reviewed

GTE Large EN v1.5 is a 1B embeddings model available through supported Badgr execution routes.

Last validated or meaningfully updated: 2026-07-27

EmbeddingsEdge-capable

Availability

Not yet Badgr AI API

Not currently validated

Not yet Dedicated endpoint

Not currently validated

Not yet Custom GPU deployment

Not currently validated

Deployment profile

Architecture: NewModel
Licence: Apache-2.0
Minimum VRAM: ~5GB
Context: 8K tokens

Recommended: RTX 4090 24GB. VRAM is an estimate and increases with context, cache, concurrency, and runtime overhead.

GPU deployment scenarios

Estimated from parameter count and quantisation. Will switch to “Verified by Badgr” once a real run backs a tier.

Testing

RTX 4090 24GB

Short context, low concurrency

Small production

L40S 48GB

8K context, 1-4 concurrent requests

Higher throughput

A100 80GB

32K context, continuous batching

Large production

RTX 4090 24GB

Higher concurrency

Estimated — multimodal inputs, long context, and concurrency all increase real VRAM use beyond this estimate.

Also runs well on L40S 48GB, A100 40GB. Available in United States, Europe.

Serving compatibility

Runtime	Status	Notes
vLLM	Estimated	Used for Badgr dedicated endpoints
Transformers	Unknown	Basic fallback
llama.cpp	Requires GGUF conversion	No GGUF weights found in repo
SGLang	Unknown	Not evaluated
Ollama	Unknown	Not evaluated
Diffusers	Unknown	Not evaluated
PyTorch	Unknown	Not evaluated
TensorRT-LLM	Unknown	Conversion may be required
TGI	Unknown	Not evaluated

Estimated pricing

Provider estimate: cold start

2–5 min

Provider estimate: endpoint cost

$0.18 – $0.44/hr

Idle cost

$0 when stopped

Per-token throughput cost is not shown here: Badgr has not benchmarked this model yet, and this page does not display figures it cannot back with real data.