macrostack
Browse

The AI stack

Categories

Local & Sovereign AINotes & KnowledgeObservability & MonitoringPassword ManagersWeb AnalyticsTeam ChatSmart HomeNetworking & RoutersVideo ConferencingCloud Storage & SyncPhotos & MediaAPI DevelopmentImage EditingWorkflow Automation & iPaaSDeveloper Tools & ContainersOffice & Productivity SuitesNo-Code DatabasesCode Hosting & Git ForgesProject ManagementEmail Marketing & NewslettersScheduling & BookingError Tracking & Exception MonitoringLog Management & SIEMVPN & PrivacyEmail & Secure MailVector Databases & AI SearchLLM & Agent FrameworksDomains & Web HostingData Removal & PrivacyAuthentication & IdentityHelp Desk & Customer SupportCloud & VPSKubernetes & Container PlatformsEmbedding ModelsPDF & DocumentsAI Coding AssistantsAI Voice & SpeechLLM Observability & EvaluationLLM Gateways & RoutingCloud GPU & AI ComputeCI/CD & build automationData & pipeline orchestrationModel serving & inferenceAI agent frameworksBackend as a serviceSecrets managementFeature flags & experimentationProduct analyticsSearch infrastructureUptime & status monitoringAffiliate & partner platformsVisitor identification & personalisationWikis & internal docsIdentity & access managementData warehouses & analytics enginesCustomer data platformsCRMObject storageBI & dashboardsE-signatureWhiteboards & diagrammingIn-memory data stores & cachingPlatform as a serviceTransactional & bulk emailHeadless CMSDesign & prototypingE-commerce platformsInternal tools & admin panelsManaged databasesForms & surveysFine-Tuning & Model TrainingRAG & Retrieval PlatformsLLM Evaluation & TestingAI Guardrails & Content SafetySpeech Recognition & TranscriptionExperiment Tracking & ML OpsDocument AI & OCRCompliance automation & security posture

About

How we rank & score

NVIDIA · Grace Blackwell · Layer 2

NVIDIA DGX Spark

Developers who want a coherent 128 GB memory space on a desk, at wall-socket power.

buy You can purchase this outright.

Specifications

Memory
128 GB unified LPDDR5X
Bandwidth
273 GB/s
Compute
GB10 Grace Blackwell superchip
Power
Roughly 170 W
Interconnect
ConnectX for pairing two units
Software
CUDA
Form factor
desktop
Workload
inference

Verified 2026-09-06. Fields reading “not published” are exactly that — we do not estimate a figure a vendor withholds.

The catch

273 GB/s is an order of magnitude below HBM. It loads big models; it does not serve them fast. Judge it as a development machine, not a server.

The economics

The cheapest legitimate route to running a 70B-class model locally with no cloud bill and no data leaving the building.

Cost per million tokens is the metric that decides most real purchases, and there is no neutral benchmark for it — every published figure comes from a company selling one side of the comparison. Read all of them, ours included, as claims rather than measurements.

The power question underneath this

A rack of frontier accelerators draws 120–200 kW against a 2026 average of about 27 kW, and the US grid interconnection queue exceeds 2,600 GW with waits approaching five years. Whether you can energise this part is now a harder question than whether you can buy it.

Layer 1 — Energy · The interconnection queue · Rack power density · Tokens per watt

Compared against

Related silicon

Macrostack does not sell silicon and takes no commission at this layer — there is no affiliate programme for compute, which is exactly why nobody publishes a neutral comparison of it.

The Macrostack brief

New swaps, worth your inbox.

A short, occasional email when we add a high-intent alternative or ship a new head-to-head. No spam, no selling your address — unsubscribe in one click.