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EU · GDPR Art. 28 · Intel TDX · OpenAI-compatible

VoltageGPU vs Groq

Groq, Inc. is a US Delaware corporation headquartered in Mountain View, CA, building proprietary LPU (Language Processing Unit) inference hardware. It is not affiliated with VoltageGPU. Groq (LPU inference) is also unrelated to Grok, the xAI chatbot operated by Elon Musk, the names are commonly confused.

Groq is faster than us. Way faster. Llama 3.3 70B streams at 500+ tokens per second on their LPU silicon; we publish no speed figure of our own, and we are not going to beat that. What we offer instead is the model running inside an Intel TDX enclave, with attestation of the serving instances you can verify; our API relay outside the enclave sees requests in clear. Groq optimises latency. We optimise trust. Different products.

Requests pass through our relay, which sees them in clear (TLS ends at our host). To keep plaintext off our servers, run your own inference server in a Confidential VM and terminate TLS inside its trust domain. Confidential VMs

Headline pricing

Per-million-token list price by model tier. VoltageGPU rows are TEE models (Intel TDX), priced from our live catalog. A dash means the competitor does not publish a comparable SKU. Live prices on /pricing.

TierVoltageGPU (TEE)Groq
Cheapest entry point
Qwen3.8-27B-TEE
live price on /pricing
Llama 3.1 8B Instant
in $0.0500 · out $0.0800 / 1M tok · cheaper and faster on raw cost, no TEE, no attestation, US-only inference path
Larger fast (70B-class flagship)
gemma-4-31B-turbo-TEE
live price on /pricing
Llama 3.3 70B Versatile
in $0.5900 · out $0.7900 / 1M tok · 500+ tok/s on LPU, best-in-market streaming latency, no TEE
Frontier MoE
Qwen3.5-397B-A17B-TEE
live price on /pricing
none
— · no comparable frontier MoE SKU on Groq today
Confidential techIntel TDX + trust-domain GPU isolationNot offered (no Intel TDX, no GPU TEE, LPU chip + standard datacenter inference)
AttestationIntel DCAPNone
BillingPer-token, OpenAI-compatiblePer-token, OpenAI-compatible (USD per 1M input/output tokens; Whisper billed per hour of audio)
OperatorVOLTAGE EI (France)Groq, Inc. (US, Delaware), Mountain View, CA HQ
Setup~30 sec, base URL and API key~30 seconds (signup + API key)
JurisdictionFrench company, GDPR Art. 28; compute at US sub-processors (SCCs)US company

Groq optimises latency. We optimise trust. Not the same product.

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 models running inside Intel TDX enclaves at our inference provider, sold by a French operator (VOLTAGE EI, SIREN 943 808 824, Solaize, France). GET /v1/attestation returns the TDX quotes and NVIDIA GPU evidence of the serving instances on your nonce; it proves those instances are TDX guests, not that a given response came from them, and our API relay sees requests in clear while forwarding them. We publish no throughput figure: expect GPU speeds, structurally slower than what Groq does on its LPU. The trade we are making is hardware isolation of the model in exchange for latency.

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 runs the model in a hardware enclave under a French controller, for workloads where the buyer wants the model isolated in hardware on top of the 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.

GDPR on the contract vs GDPR in the silicon

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, some buyers want the technical measures backed by hardware isolation, not only by documented controls. That is the boundary Intel TDX was built for; on VoltageGPU it covers the model, not our relay, which sees requests in clear.

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 and the silicon route cover different things. On VoltageGPU the model runs in Intel TDX, and GET /v1/attestation returns the TDX quotes of the serving instances, which an auditor can re-verify offline against the Intel root certificate; there is no quote for each call. The operator entity on the contract is a French sole proprietorship; our inference provider is in the US (listed on /legal/subprocessors), and our relay sees requests in clear. Either side of that line can be the rational choice; the page makes the line visible before the procurement decision.

Where Groq wins, and it is not small

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. 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. The honest summary: Groq is the speed leader, the cheap-end leader on Llama 8B, and the broader API surface today; VoltageGPU runs the model in a hardware enclave. Pair them, do not choose between them, if the workload portfolio spans both regulated and non-regulated tiers.

FAQ

Is Groq the same as Grok (Elon Musk's xAI chatbot)?

No, and this is one of the most common confusions in the space. Groq, Inc. is a US Delaware corporation headquartered in Mountain View, CA, founded in 2016, building proprietary LPU (Language Processing Unit) silicon for inference and exposing an OpenAI-compatible API at api.groq.com. Grok is a chatbot product operated by xAI, the AI company founded by Elon Musk, and is unrelated to Groq, Inc. The substring overlap in the names is genuinely unfortunate and search results often mix the two. VoltageGPU is not affiliated with either Groq, Inc. or with xAI / Grok.

Is Groq faster than VoltageGPU?

Yes, by a wide margin on tokens-per-second throughput. Groq's LPU silicon delivers 500-plus tokens per second on Llama 3.3 70B Versatile and 800-plus tokens per second on Llama 3.1 8B Instant, those are industry-leading numbers nobody else publishes today. VoltageGPU publishes no throughput figure; our models run on GPUs inside Intel TDX enclaves. We are not going to beat Groq on streaming latency and we are not going to pretend otherwise. The trade VoltageGPU offers is the model in a hardware enclave, with attestation of the serving instances, in exchange for that latency. For consumer voice agents and real-time streaming UX, Groq is the correct choice. For workloads where you want the model in a hardware enclave, VoltageGPU is the better fit. The two products are not direct substitutes; many serious buyers run both.

Does Groq offer confidential computing or TEE attestation?

No. Groq's product is built around proprietary LPU silicon optimised for inference latency and is served from standard datacenter infrastructure with SOC 2 Type II controls on the operational side. There is no Intel TDX equivalent, no GPU TEE, no trust-domain GPU passthrough encryption, and no attestation quote rooted in a hardware certificate authority. That is not a Groq bug, it is what their product was designed to be. For the workloads where SOC 2 plus a DPA is the correct regulatory posture (the majority of inference workloads, including most public-data RAG, code generation, summarisation, and evaluation), Groq is a perfectly correct choice. For workloads where the buyer wants the model isolated in hardware, bar-association secrecy, HDS, MiFID II, PCI DSS, GDPR Article 9 sensitive data, VoltageGPU runs the model in Intel TDX; our relay still sees requests in clear, so judge whether that meets your obligations.

Does Groq have EU data centers for GDPR-resident inference?

As of May 2026 Groq's primary inference footprint is US-anchored, with EU capacity expanding but not yet positioned as a GDPR-native region with the same throughput SLA as the US fleet. The legal operator is Groq, Inc., a US Delaware corporation subject to the Cloud Act, which is the structural reason Cloud Act exposure remains a recurring concern in European procurement reviews of US-headquartered inference providers. For non-sensitive workloads the standard DPA framework is sufficient. VoltageGPU's operator is VOLTAGE EI in Solaize, France (SIREN 943 808 824), but our inference provider is in the US too (listed on /legal/subprocessors): what we add is the model inside Intel TDX, with GET /v1/attestation returning the TDX quotes of the serving instances, re-verifiable offline against the Intel root certificate. It is not an EU-residency answer for inference. Buyers running both regulated and non-regulated workloads can route the regulated tier to VoltageGPU and the latency-sensitive tier to Groq.

Can I use Groq for HIPAA workloads?

Groq has SOC 2 Type II certification, which is the formal baseline US enterprise buyers expect, but HIPAA Business Associate Agreement availability has historically been gated and buyers should confirm BAA terms directly with Groq sales before processing PHI. For US covered entities working with de-identified data or with appropriate scrubbing pipelines, Groq's posture is consistent with the rest of the US inference market. For PHI, the architectural alternative is a model running in a hardware enclave: VoltageGPU runs it in Intel TDX at our US inference provider, our relay sees requests in clear, and we offer no BAA yet. Groq and VoltageGPU therefore sit at different tiers of the same compliance ladder; neither is the wrong answer in absolute terms, but the correct one depends on the data classification.

Other inference APIs compared

Fastest LPU or confidential GPU, pick the right tool

Groq is the right answer when streaming latency is the killer feature. VoltageGPU is the right answer when you want the model in a hardware enclave. Many buyers can run both. Get an API key or read the full attestation architecture.

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