Small Language Models (SLMs)
Domain-specific, cost-efficient SLMs for sensitive workloads.
AiCX's Small Language Models (SLMs) practice deploys domain-specific, cost-efficient language models — fine-tuned and quantized models that run at the edge or on-prem for sensitive workloads, low-latency requirements, or radically lower per-call cost than frontier LLMs.
Frontier LLMs are powerful but expensive, slow at scale, and require sending data outside your environment. For many workloads — classification, summarization, intent detection, structured extraction, narrow conversational tasks — a well-tuned 7B–14B parameter model running on your own infrastructure delivers comparable quality at a fraction of the cost.
We deploy SLMs (Llama, Mistral, Qwen, Phi, Gemma, plus fine-tuned variants) on managed Kubernetes, edge compute, and on-prem hardware. We handle fine-tuning, evaluation, quantization, serving infrastructure, and ongoing operations — turning open-source models into production assets.
The difference is in how we run the program — not the deck.
Plenty of vendors can quote you a seat. Few can deliver an outcome. Here's what changes when AiCX runs your small language models (slms) program.
Radically lower cost
80–95% cheaper per call than frontier LLM APIs at production scale.
Data stays in your environment
On-prem and private cloud deployment for sensitive workloads — no data leaves your perimeter.
Sub-200ms latency
Fast enough for real-time agent assist, IVA turn-taking, and streaming use cases.
Fine-tuning expertise
Domain-specific fine-tuning, RLHF, and instruction tuning for narrow workloads where it pays off.
Quantization and serving
GGUF, AWQ, GPTQ quantization plus vLLM, TGI, TensorRT-LLM serving for efficient inference.
Edge deployment
Models running on edge hardware for latency-sensitive or air-gapped environments.
Everything you need on day one — built in.
A small language models (slms) program from AiCX ships with the operational scaffolding most clients spend quarters trying to assemble in-house.
- Open-source model selection (Llama, Mistral, Qwen, Phi, Gemma, others)
- Domain-specific fine-tuning
- Instruction tuning and RLHF
- Quantization (GGUF, AWQ, GPTQ, FP8)
- Inference serving (vLLM, TGI, TensorRT-LLM, llama.cpp)
- Multi-tenant model serving
- Eval harness and continuous quality monitoring
- Cost monitoring and per-call accounting
- On-prem deployment (GPU, CPU)
- Private cloud deployment (AWS/Azure/GCP)
- Edge deployment for latency-sensitive use
- Model lifecycle management (versioning, rollback)
How teams put small language models (slms) to work.
On-prem PHI-aware classification
Deployed fine-tuned 8B model on-prem for HIPAA-sensitive classification at 4M docs/month with sub-100ms latency.
Domain-specific summarization
Fine-tuned 13B model for compliance-adjacent summarization; matched GPT-4 quality at 8% of the cost.
Real-time intent at scale
Deployed quantized 7B model for real-time intent detection at sub-50ms latency across 8M monthly conversations.
Common questions about Small Language Models (SLMs).
Don't see your question? Talk to our solutioning team — we'll walk you through pricing, footprint, and ramp options for your specific program.
Related services
AI Applications & Managed Services
LLM assistants, RAG pipelines, classification models — operated as a service.
API Integration Tools
Pre-built and custom integrations across CCaaS, CRM, ticketing, and telephony.
BOT Development
Conversational and task-automation bots with ongoing tuning.
Ready to deploy Small Language Models (SLMs)?
Schedule a 30-minute working session with our solutioning team — bring your KPIs, leave with a delivery plan.