VoltageGPU vs Groq

Groq built a proprietary chip — the LPU, Language Processing Unit — specifically to remove every latency bottleneck in transformer inference that a general-purpose GPU was never designed to remove. The architecture is deterministic, the memory layout is single-die SRAM, the compiler ships the entire model graph statically, and the result is the fastest open-weight inference numbers any third party can independently reproduce. Llama 3.3 70B Versatile streams at 500-plus tokens per second on Groq today; Llama 3.1 8B Instant pushes past 800 tokens per second. For a voice assistant that has to start speaking before the user finishes their sentence, for an agentic loop where tool-calls happen at the speed of human thought, for a streaming chat UI where time-to-first-token under 100ms is the killer feature, Groq is the correct choice and is not close to being beaten on that axis.

VoltageGPU optimises a different axis. The product is Confidential AI Infrastructure: open-weight TEE-attested inference running inside Intel TDX guest VMs on European hardware, with NVIDIA Protected PCIe between CPU and GPU, an Intel DCAP attestation quote per session, and the operator (VOLTAGE EI, SIREN 943 808 824, Solaize, France) mathematically constrained from reading workload memory. We run open models like Qwen3-32B at roughly 80–150 tokens per second — perfectly adequate for chat and document workflows, structurally slower than what Groq does on the same model class. The trade we are making is silicon-enforced isolation in exchange for latency, on EU jurisdiction in exchange for US-anchored LPU throughput.

The honest framing is product-shape, not provider-quality: Groq is the fastest open-weight inference API on the market and the right answer for any latency-sensitive consumer-facing or real-time workload; VoltageGPU is hardware-attested confidential inference on EU infrastructure and the right answer for any regulated workload where the technical measures clause of a GDPR Article 28 DPA needs to be enforced by hardware rather than by contract. Many serious buyers will end up using both — Groq for the consumer-facing voice agent that streams Llama responses at 500 tok/s, VoltageGPU for the back-office confidential chatbot that processes client files for a law firm under bar-association secrecy. The page exists so that pairing is visible as a strategy rather than as a confusion.

Groq operates US-anchored inference datacenters and is SOC 2 Type II audited, which together cover the formal baseline most US enterprise buyers expect from an inference provider. EU residency is limited as of May 2026: capacity is expanding but there is no GDPR-native EU region with the same throughput SLA as the primary US footprint, and the legal operator is a Delaware corporation subject to the Cloud Act. For the majority of inference workloads — public-data summarisation, RAG over non-sensitive corpora, code generation, marketing copy, evaluation suites — that posture is sufficient and the speed advantage is the right reason to choose Groq.

It runs out of room exactly where the workload becomes confidential. A SOC 2 report attests that the operator has documented controls; it does not constrain the host administrator from inspecting workload memory at runtime. For client files protected by professional secrecy (RIN art. 2.2 for French avocats), for patient records under HDS-certified processing, for cardholder data under PCI DSS scope, for sensitive personal data under GDPR Article 9, CNIL and equivalent European authorities have started to require the technical measures clause be backed by hardware evidence that the operator cannot read the memory. That is the boundary Intel TDX and NVIDIA Protected PCIe were built to satisfy.

VoltageGPU's answer is therefore not "Groq is non-compliant" — the SOC 2 posture is correct for the workloads it is correct for. Our answer is that the DPA route runs out of room exactly where the silicon route begins. Every confidential inference call on VoltageGPU produces an Intel DCAP attestation quote that an auditor can re-verify offline against the Intel root certificate. The data physically does not leave French infrastructure. The encrypted-memory key is ephemeral and per-VM. The operator entity is a French sole proprietorship inside European jurisdiction. Article 28 is enforced at the silicon layer, not at the contract layer. Either side of that line can be the rational choice; the page makes the line visible before the procurement decision.

It is not a marketing exercise to admit a competitor is the right tool for many jobs. Groq is the right tool for many jobs, and a fair comparison page has to say where. On raw streaming latency Groq is the global leader on open-weight models: Llama 3.3 70B Versatile at 500-plus tokens per second is a number nobody else publishes today, and Llama 3.1 8B Instant at 800-plus tokens per second is faster than most users can read text on a screen. For consumer voice agents, real-time translation, streaming chat with sub-100ms time-to-first-token, and agentic loops where the loop itself blocks on inference, Groq is the correct architectural choice and no amount of TEE marketing changes that.

On price-per-token at the cheap end of the catalogue Groq is also unbeatable. Llama 3.1 8B Instant at $0.05 per million input tokens and $0.08 per million output tokens is one of the cheapest fast Llama inference SKUs on the market globally; we do not have a sub-$0.10 tier and we are not going to pretend we do. For high-volume non-sensitive workloads — public-content classification, RAG over open corpora, code suggestions, marketing draft generation — running on Groq Llama 3.1 8B is the rational cost-optimised path and switching to TDX-attested inference for those workloads would be paying for a guarantee the workload does not need.

Groq also ships product surface we do not match today. Whisper-large-v3 speech-to-text at $0.111 per hour of audio is a category VoltageGPU does not offer at all as of May 2026. Tool-calling and structured output on Llama 3.3 70B at LPU throughput is an agent-loop primitive Groq has invested in heavily. Qwen-QwQ-32B preview and DeepSeek-R1-Distill-Llama-70B are reasoning models Groq exposes that we are still evaluating for confidential deployment. The honest summary: Groq is the speed leader, the cheap-end leader on Llama 8B, and the broader API surface today; VoltageGPU is the confidential-inference leader on EU jurisdiction. Pair them, do not choose between them, if the workload portfolio spans both regulated and non-regulated tiers.