Home/Products/Bud Latent
Inference · Embeddings

Production inference for embeddings, reranking and retrieval.

Bud Latent is one serving plane for embeddings, reranking, classification and multi-modal retrieval — a production-grade pipeline built on the Infinity framework, running the same way on a laptop and a datacentre cluster.

Talk to a solutions architect View benchmarks
280×
Faster tokenization vs. HuggingFace Tokenizers, using the BudTikTok SIMD implementation
+67%
Throughput improvement from token-budget batching vs. fixed-size batching
32.5%
Average cache hit rate across hybrid L1 in-memory and L2 Redis/DiskANN caching
95%+
GPU utilization held under sustained production load
One serving plane

Every modality and every task, in one deployment.

Latent unifies embedding, classification and reranking across all data types, so a single deployment covers what would otherwise be several serving stacks.

Modalities

TextImageAudioDocumentVision

Task types

EmbeddingsClassificationRegressionRerankingNER / QADense features

Domain hints

SafetyToxicityIntentLanguage IDSemantic search
Model coverage

Drop-in support for the models you already use.

Text embeddings

BGEGTEall-MiniLMInstructorNomic

Vision models

CLIPSigLIPColPaliColQwen2DINOv3

Audio models

CLAPlaion/clap-htsatWhisper

Document models

ColPaliColIdefics2model2vec
Production use cases

From RAG pipelines to real-time safety.

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

Each component answers a specific production problem.

ProblemVariable-length inputs waste GPU cycles
SolutionToken-budget batching
Result+67% throughput
ProblemTokenization is the bottleneck
SolutionBudTikTok SIMD tokenizer
Result280× faster tokenization
ProblemExtreme load causes latency spikes
SolutionHACC admission scheduling
ResultProtected p99 at 1000+ RPS
ProblemDuplicate workloads waste compute
SolutionHybrid L1/L2 semantic cache
Result32.5% cache hit rate
ProblemCustom logic needs extensibility
SolutionPlugin architecture
ResultSafe, sandboxed customization
Performance benchmarks

Measured across workloads and hardware.

Each figure below states the configuration it was measured on — the numbers move with your model, batch shape and hardware.

280×
Faster tokenization compared to HuggingFace Tokenizers.
BudTikTok SIMD + parallel multi-process
+67%
Throughput improvement over fixed-size batching.
Token-budget batching + HACC scheduling
32.5%
Average cache hit rate — 20% from L1 memory, 12.5% from L2 disk — for a 1.45× speedup on hits.
Hybrid L1 in-memory + L2 Redis/DiskANN
<10ms
p99 latency for embedding generation at 1000+ requests per second, a 30% p99 reduction.
HACC admission control
Hardware reach

600+ hardware targets, one compiler backend.

The Infinity compiler backend is what lets the same deployment description run on an edge device and a multi-node cluster.

Accelerator types

GPUs, TPUs, NPUs, IPUs, FPGAs.

Cloud & datacenter

AWS, GCP, Azure, on-prem.

Edge & client

Laptops, mobile, embedded.

Heterogeneous

Mix CPU, GPU and NPU in one cluster.

Prometheus + OTELTLS / mTLSAudit logsEncryption at restMulti-model serving

Start with an OpenAI-compatible /v1/embeddings endpoint.

Or deploy distributed inference on Kubernetes with the Helm charts. A solutions architect can size it against your retrieval workload.

Talk to a solutions architect See Bud Cache