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Compound AI Inference

Bud Latent

Production-grade inference for embeddings, reranking, classification, and multi-modal retrieval. One unified serving system for all your AI workloads.

Get Started View Benchmarks
Chunk
→
Tokenize
→
Batch
→
Infer
→
Post
→
Cache
280x
Faster Tokenization
vs HuggingFace Tokenizers
+67%
Throughput Improvement
With token-budget batching
32.5%
Cache Hit Rate
Hybrid L1/L2 caching
95%+
GPU Utilization
Maximized hardware efficiency

How Does Latent Bud Compare?

See detailed feature comparisons against TEI, Infinity, and Baseten BEI. Calculate your potential TCO savings.

View Comparison & Calculator

System Architecture

Production-grade inference pipeline built on the Infinity framework. Click any component to explore. Explore Disaggregated Architecture →

LATENT BUD — COMPOUND AI INFERENCE SYSTEM INPUTS & PROTOCOLS T Text I Image A Audio M Multi-Modal PROTOCOLS OpenAI Compatible gRPC Streaming PLUGIN SYSTEM Hot Reload Pre/Post Processing Cache · Scheduler · API ColBERT · model2vec PRE-PROCESSING & ORCHESTRATION TOKENIZATION BudTikTok SIMD 280x faster · AVX-512/NEON CHUNKING Chonkie Backend 6 strategies · Overlap refinery BATCHING & SCHEDULING HACC: Token-Budget Batching · Tiered Priority Queues · +67% throughput INFERENCE CORE AsyncEmbeddingEngine Hardware Executor: Dynamic · Packed · FlashAttention · CUDA Graph PyTorch ONNX CTranslate2 Neuron DINOv3 CACHE LAYER L1 Memory + L2 Disk · 32.5% hit rate HARDWARE TARGETS GPU · CPU · TPU · NPU · 600+ SKUs POST-PROCESSING: Pooling · Normalization · Quantization · Matryoshka Slicing Data flows through pipeline with 95%+ GPU utilization OUTPUTS Embeddings Float, Int8, Binary Rerank Scores Cross-encoder Classifications Multi-label Dense Features DINOv3 OBSERVABILITY & OPS Prometheus + OTEL TLS/mTLS Audit Logs Auto-scaling Health Checks Structured Logging

One serving plane for every modality and task

Latent Bud unifies embedding, classification, and reranking across all data types in a single deployment.

5

Modalities

Text Image Audio Document Vision
6

Task Types

Embeddings Classification Regression Reranking NER/QA Dense Features
10+

Domain Hints

Safety Toxicity Intent Language ID Semantic Search

Extensive Model Coverage

Drop-in support for the most popular embedding and transformer models.

Text Embeddings

BGE E5 GTE all-MiniLM Instructor Nomic

Vision Models

CLIP SigLIP ColPali ColQwen2 DINOv3

Audio Models

CLAP laion/clap-htsat Whisper

Document Models

ColPali ColIdefics2 model2vec

Built for Production Use Cases

From RAG pipelines to real-time safety systems, Latent Bud handles it all.

RAG & Search Retrieval

  • High-throughput document embedding
  • OpenAI-compatible /v1/embeddings API
  • Semantic caching for repeated queries

Reranking at Scale

  • Cross-encoder reranking
  • Optimized for 1000+ candidates/sec
  • Sub-10ms p99 latency

Universal Prediction

  • Sentiment analysis
  • Multi-label classification
  • Intent detection

Multimodal Retrieval

  • Image-to-text search
  • Audio similarity matching
  • Document understanding

Guardrails & Safety

  • Toxicity detection
  • PII identification
  • Content moderation

High-QPS Applications

  • Protected p99 at 1000+ RPS
  • HACC admission control
  • Auto-scaling ready

Why Latent Bud?

Every component is optimized for production AI workloads.

Problem
Variable-length inputs waste GPU cycles
→
Solution
Token-budget batching
→
Result
+67% throughput improvement
Problem
Tokenization is the bottleneck
→
Solution
BudTikTok SIMD tokenizer
→
Result
280x faster tokenization
Problem
Extreme load causes latency spikes
→
Solution
HACC admission scheduling
→
Result
Protected p99 at 1000+ RPS
Problem
Duplicate workloads waste compute
→
Solution
Hybrid L1/L2 semantic cache
→
Result
32.5% cache hit rate
Problem
Custom logic needs extensibility
→
Solution
Plugin architecture
→
Result
Safe, sandboxed customization

Performance Benchmarks

Rigorously tested across diverse workloads and hardware configurations.

280x 140x 70x 1x Latent Bud HuggingFace 280x 1x
280x
Faster tokenization compared to HuggingFace Tokenizers using BudTikTok SIMD implementation
BudTikTok SIMD + Parallel Multi-Process
Latent Bud SentenceTransformers 1,840 texts/s 557 texts/s 3.3x faster
+67%
Throughput improvement with token-budget batching vs fixed-size batching
Token-Budget Batching + HACC Scheduling
32.5% Hit Rate
L1 Memory (20%)
L2 Disk (12.5%)
32.5%
Average cache hit rate with hybrid L1 in-memory and L2 Redis/DiskANN caching
1.45x speedup from cache hits
95%+ GPU Utilization 0% 100%
<10ms
p99 latency for embedding generation at 1000+ requests per second
-30% P99 latency with HACC

600+ Hardware Targets

Run anywhere with the Infinity compiler backend.

Accelerator Types

GPUs, TPUs, NPUs, IPUs, FPGAs

Cloud & Datacenter

AWS, GCP, Azure, on-prem

Edge & Client

Laptops, mobile, embedded

Heterogeneous

Mix CPU + GPU + NPU in one cluster

Prometheus + OTEL
TLS / mTLS
Audit Logs
Encryption at Rest
Multi-model Serving

Start with OpenAI-compatible /v1/embeddings

Or deploy distributed inference on Kubernetes with Helm charts.

Read the Docs View on GitHub
View Benchmarks | Compare Solutions | Architecture Deep-Dive

Democratizing GenAI by commoditizing it.

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