Groq LPU vs Nvidia GPU: Inference Speed Benchmarks
Groq LPU vs Nvidia GPU compared for LLM inference speed in 2026 — tokens per second benchmarks, latency, throughput, pricing, availability, model support, and which to use for production AI applications.
Quick Answer
Groq LPU (Language Processing Unit) delivers 10–18x faster token generation than equivalent Nvidia GPU deployments for autoregressive LLM inference — 800+ tokens/second on Llama 3 70B vs ~60–80 t/s on A100. However, Groq's hardware is cloud-only API access, not purchasable. For self-hosted inference, Nvidia GPUs remain the only practical option in 2026.
Groq LPU (via GroqCloud API) vs Nvidia GPU (A100/H100 Cloud): Overview
Ultra-fast LLM inference APIs, latency-critical production applications, agentic pipelines
Yes (rate-limited free tier on GroqCloud)
Llama 3 70B: $0.59/M input tokens, $0.79/M output tokens (2026 pricing)
Nvidia GPU (A100/H100 Cloud)
Industry-standard ML accelerators powering most production AI infrastructure
Fine-tuned model hosting, arbitrary model support, self-hosted inference, training
Depends on provider (Lambda Labs, RunPod, AWS offer trial credits)
A100 80 GB: $1.49–$2.49/hr (RunPod/Lambda) · H100 SXM: $2.49–$3.50/hr
Groq LPU (via GroqCloud API) vs Nvidia GPU (A100/H100 Cloud): Feature Comparison
| Feature | Groq LPU (via GroqCloud API) | Nvidia GPU (A100/H100 Cloud) |
|---|---|---|
| Llama 3 70B Tokens/sec | ~800–1,000 t/s | ~60–150 t/s (A100/H100) |
| First Token Latency | <100ms | 200–800ms |
| Custom/Fine-tuned Models | No (certified models only) | Yes (any model) |
| Self-hosted Option | No (cloud API only) | Yes (on-premise) |
| Llama 3 70B Output Pricing | $0.79/M tokens | ~$0.90–$1.50/M tokens equiv. |
| Context Length (max) | 8K–128K (model-dependent) | 128K–1M (model-dependent) |
Pros & Cons
Groq LPU (via GroqCloud API)
Pros
- 800–1,000 t/s on Llama 3 70B: 10–15x faster than A100 GPU, 50x faster than RTX 4090 local inference
- First token latency <100ms: dramatically better user experience for streaming chat applications
- Deterministic throughput: SRAM-based architecture has no memory bandwidth variance
- Competitive pricing: often cheaper than equivalent A100/H100 GPU cloud at standard workloads
- OpenAI-compatible API: drop-in replacement with `base_url` change — no code refactor needed
Cons
- Cloud-only: no on-premise option — all data leaves your infrastructure
- Limited model support: only Meta Llama, Mixtral, and a few Groq-certified models; no arbitrary GGUF/ONNX
- Context window limits: hardware-imposed context limits on some models (e.g. 8K on older deployments)
- No fine-tuned model hosting: you cannot deploy a LoRA-fine-tuned model on Groq — only base models
- Vendor lock-in for latency: if Groq changes pricing or availability, no self-hosted fallback matches speed
Nvidia GPU (A100/H100 Cloud)
Pros
- Universal model support: any HuggingFace model, GGUF, ONNX, TensorRT, custom architectures
- Self-hosted option: run on-premise for data privacy, compliance, or long-term cost savings
- Fine-tuned model deployment: host your LoRA/QLoRA fine-tunes using vLLM or TGI
- Scale-out: 8× H100 NVLink cluster for serving 405B+ models or parallel training
- Mature tooling: vLLM, TGI, Triton Inference Server, NVIDIA NIM all production-ready
Cons
- Inference speed: H100 achieves ~100–150 t/s on Llama 3 70B — Groq is 6–10x faster
- First token latency: 200–800ms for 70B models vs Groq's <100ms
- Higher per-token cost for latency: matching Groq's throughput requires multi-GPU inference serving
- Cold start: container-based deployments have 30–90s cold starts for serverless patterns
Our Verdict: Groq LPU (via GroqCloud API) vs Nvidia GPU (A100/H100 Cloud)
Use Groq for latency-critical chat and agentic applications where first-token speed directly affects user experience — it is genuinely unmatched for streaming standard Llama/Mixtral models. Use Nvidia GPU infrastructure when you need custom or fine-tuned models, on-premise data residency, or context windows beyond Groq's current limits. Many production systems combine both: Groq for the user-facing fast path, GPU cluster for fine-tuned or private model inference.
Groq LPU (via GroqCloud API) vs Nvidia GPU (A100/H100 Cloud) — FAQs
What is an LPU and how does it differ from a GPU?
A Language Processing Unit (LPU) is Groq's custom ASIC designed specifically for the sequential, autoregressive token generation pattern of transformer models. Unlike GPUs which use DRAM for model weights (bandwidth-limited during inference), Groq's LPU stores the entire model weights in on-chip SRAM — eliminating memory bandwidth bottlenecks entirely. The tradeoff: SRAM is expensive and doesn't scale easily, so LPUs are large chips supporting only specific model sizes and cannot be used for arbitrary matrix operations like GPU training.
Is Groq available for enterprise on-premise deployment?
As of 2026, Groq offers on-premise GroqRack hardware for enterprise customers — custom data center deployments with dedicated LPU clusters. Pricing is contract-based and targeted at large enterprises. For most startups and mid-size companies, GroqCloud API (pay-as-you-go) is the accessible option. Groq has announced intent to expand hardware availability, but individual purchasable LPU cards do not exist — unlike Nvidia consumer GPUs.
How does Groq compare to Cerebras for LLM inference?
Cerebras CS-3 (a wafer-scale engine) takes a similar SRAM-centric approach to Groq, storing model weights on-die. Cerebras has demonstrated even higher token throughput on smaller models (Llama 3 8B at 2,000+ t/s) and supports training workloads — something Groq currently does not. Groq's advantage is API availability and pricing transparency for inference; Cerebras is more focused on large enterprise training contracts. Both are faster than GPU for autoregressive inference but remain cloud/enterprise-only in 2026.
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