Sambanova Guide

---
name: sambanova-guide
description: Run open models on SambaNova's reconfigurable hardware — fast inference with enterprise deployment options.
category: ai-research
---

## Overview

SambaNova builds reconfigurable dataflow hardware (RDUs) for AI and offers
inference services running open models on it — combining competitive inference
speed with enterprise-oriented deployment options (including on-premises and
sovereign deployments for regulated industries). The positioning blends
performance with enterprise requirements: data control, compliance, and
deployment flexibility alongside fast tokens.

For most builders, SambaNova enters consideration in two scenarios: as another
fast-inference API for open models (benchmark it with the other speed
specialists), and — more distinctively — when enterprise constraints (data
residency, on-prem, regulated industries) rule out pure public-API approaches.
The reconfigurable hardware story matters less to API users than the deployment
flexibility does.

Evaluate SambaNova on your constraints: if you need speed plus enterprise
deployment options, it's distinctive; if you just need fast tokens, benchmark
it against the field.

## When to use

- Enterprise deployments with data-residency or on-premises requirements.
- Regulated industries (finance, healthcare, government) needing controlled AI
  infrastructure.
- Fast open-model inference where deployment flexibility matters.
- Benchmarking speed-optimized inference providers on your workload.
- Sovereign AI initiatives requiring domestic/controlled infrastructure.
- Hybrid deployments: public API for dev, controlled deployment for production.

## Core concepts

- **Reconfigurable dataflow architecture**: SambaNova's RDUs reconfigure their
  dataflow for different models/workloads — the hardware adapts rather than
  forcing software to adapt to fixed silicon. For API users, this surfaces as
  broad model support with good performance.
- **Inference API**: cloud API for open models on SambaNova hardware. The
  standard evaluation path — benchmark speed, quality, and cost.
- **Enterprise deployment options**: on-premises and private-cloud deployments
  for organizations that can't send data to public APIs. This is the
  differentiator — understand the engagement model (it's enterprise sales, not
  self-serve signup).
- **Data control**: keeping inference inside your boundary for compliance and
  IP protection. Relevant when prompts contain sensitive data that can't leave
  the organization.
- **Model support**: open models compiled for the RDU architecture. Check which
  models you need are supported in your deployment mode.
- **Performance profile**: competitive inference speed from the dataflow
  architecture. Measure on your workload — architecture advantages are
  workload-dependent.
- **Full-stack offering**: SambaNova also offers training systems and broader
  platform components. API users can ignore this; enterprise buyers evaluate the
  stack.
- **Compliance posture**: certifications and controls relevant to regulated
  buyers. Verify the specific compliance artifacts your organization requires.

## Practical workflow

1. **Clarify your constraints.** Is this about speed, data control, compliance,
   or all three? The answer determines whether you evaluate the API, the
   enterprise deployment, or both.
2. **Benchmark the API.** If using cloud inference: same evals as any provider —
   quality, latency, cost per task on your workload.
3. **Evaluate deployment models.** For enterprise needs: understand on-prem vs.
   private cloud options, hardware requirements, support model, and total cost
   (it's a systems purchase, not just API pricing).
4. **Verify compliance artifacts.** Collect the certifications and controls
   documentation your compliance team requires before committing.
5. **Pilot with real data.** Run a bounded pilot on representative workloads —
   including your compliance and operational requirements, not just model
   quality.
6. **Plan data flows.** Map exactly where prompts and data travel in the chosen
   deployment model. Verify against your data policies.
7. **Negotiate SLOs.** Enterprise deployments come with support agreements —
   define latency, availability, and support expectations contractually.

Checklist for a SambaNova evaluation:
- Constraints clarified (speed vs. data control vs. compliance).
- API benchmarked on your workload (if applicable).
- Deployment model understood including total cost.
- Compliance artifacts collected and approved.
- Pilot run on representative data and requirements.

## Common pitfalls

- **Evaluating only the API.** If your need is enterprise deployment, API
  benchmarks don't answer the real questions (data flows, support, total cost).
  Evaluate the deployment you'll actually buy.
- **Underestimating enterprise complexity.** On-prem AI infrastructure is a
  systems project — hardware, networking, operations, support. Budget
  accordingly.
- **Compliance assumptions.** Assuming certifications cover your specific
  requirements. Verify artifact by artifact with your compliance team.
- **Speed-only comparison.** Benchmarking SambaNova purely on tokens/sec
  against pure-play speed providers misses its enterprise differentiation —
  and vice versa.
- **Data-flow vagueness.** Not mapping exactly where data travels. For
  regulated use, "probably stays in our boundary" isn't an answer — verify.
- **Pilot scope too narrow.** Piloting model quality without testing
  operational requirements (monitoring, support responsiveness, upgrade
  procedures).
- **Ignoring total cost.** Enterprise hardware + support + operations vs.
  API pricing are different economic universes. Model the full cost.
- **Hardware novelty risk.** Reconfigurable architectures are less battle-tested
  than GPUs at scale. Weight operational maturity in the decision.