You submit a prompt. You wait. The spinner spins. Tokens trickle in. For a single question, the wait is tolerable. For generating 50 analysis reports in a batch – company research, market analysis, partnership assessments – the wait is the bottleneck. The AI is not slow because it is thinking hard. It is slow because the hardware underneath is fighting a physics problem called the memory wall.
Groq built a chip that eliminates that wall. The result: 800-1,600 tokens per second where GPUs deliver 150-300. Not a marginal improvement – a category change in what becomes practical when inference is fast and cheap.
The Memory Wall Problem
Every LLM inference provider – OpenAI, Anthropic, Google, every GPU cloud – runs on NVIDIA GPUs. The H100 and H200 are extraordinary chips for training models. For inference (generating text token by token), they have a fundamental limitation.
GPU inference is memory-bandwidth-bottlenecked. The model weights sit in HBM (High Bandwidth Memory) attached to the GPU. During text generation, the chip needs to read those weights for every single token. The H100 delivers ~3.35 TB/s of memory bandwidth. Sounds fast. It is not fast enough – the compute cores sit idle 60-70% of the time, waiting for data to arrive from memory.
This is not a software problem. It is a physics problem. No amount of optimization, batching, or clever scheduling changes the fact that the GPU’s compute is starved by its memory pipe.
How Groq’s LPU Breaks the Wall
Groq’s Language Processing Unit takes a different approach. Instead of storing model weights in external memory (HBM), the LPU stores them in on-chip SRAM – hundreds of megabytes of memory built directly into the silicon.
The numbers:
| Groq LPU | NVIDIA H100 | |
|---|---|---|
| Memory bandwidth | ~80 TB/s (on-chip SRAM) | ~3.35 TB/s (HBM) |
| Compute utilization during inference | ~100% | 30-40% |
| Execution model | Deterministic, statically scheduled | Dynamic, batch-dependent |
| Latency predictability | p99 within 15% of median | Highly variable |
| Energy per token | ~10x more efficient | Higher power draw |
The bandwidth advantage is roughly 24x. The compute cores never wait for data. The result is near-100% hardware utilization during inference – the chip does useful work on almost every clock cycle.
Deterministic scheduling is the other key difference. GPUs use dynamic scheduling – hardware queues arbitrate access to compute resources at runtime, introducing unpredictable latency. The LPU uses static scheduling determined at compile time. The compiler maps every operation to a specific time slot on a specific core. Execution is clockwork – the same prompt produces the same latency every time, within 15% variance at p99.
Scaling: When a model is too large for one LPU, Groq connects hundreds of chips using a plesiosynchronous chip-to-chip protocol. The entire rack acts as a single compute unit – no NVLink bottlenecks, no GPU-to-GPU communication overhead.
Speed Benchmarks: The Numbers
Independent benchmarks from ArtificialAnalysis.ai and multiple providers confirm Groq’s speed advantage:
Single-stream output speed:
- Llama 4 Scout on Groq: ~1,200-1,580 tokens/second
- Llama 3.3 70B on Groq: 800+ tokens/second
- Llama 3.3 70B on H100 GPU: 150-200 tokens/second
End-to-end latency (Llama 3.1 70B, 100 tokens):
- Cerebras: 574ms (fastest overall)
- Groq: 851ms
- FriendliAI: 1,041ms
- Together.ai: 1,659ms
- Fireworks: 1,864ms
Time-to-first-token:
- Groq Llama 3.3 70B: 88ms
- Groq Llama 3.1 8B: 111ms
- Most GPU providers: 300-800ms
Groq is 3-18x faster than other cloud inference providers depending on model and workload. For text generation – the sequential, token-by-token process that GPUs handle worst – the advantage is most pronounced.
The NVIDIA Deal
In December 2025, NVIDIA entered a ~$20 billion non-exclusive licensing agreement for Groq’s inference technology. Groq’s founder and key engineers moved to NVIDIA. GroqCloud continues operating independently under CEO Simon Edwards.
What this means:
- NVIDIA validated the architecture. The company that dominates AI hardware paid $20 billion for Groq’s approach to inference. That is not an acqui-hire – it is an admission that GPUs are not optimal for inference.
- GroqCloud remains operational. The API, the Batch API, the developer tools – all still running and accepting new customers.
- Long-term trajectory is uncertain. NVIDIA may integrate LPU concepts into future GPU designs. GroqCloud may eventually be absorbed. For now, it is the fastest inference API available.
- The deal is non-exclusive. Groq can still license its technology to other parties.
OpenAI has reportedly expressed dissatisfaction with NVIDIA GPUs for inference workloads. The Groq acquisition signals that NVIDIA knows GPU-based inference is not the endgame.
GroqCloud: Pricing and Access
Groq uses pay-per-token pricing that is competitive with GPU-based providers – and dramatically faster.
Per million tokens:
| Model | Input | Output |
|---|---|---|
| Llama 3.1 8B | $0.06 | $0.08 |
| GPT-OSS 20B | ~$0.13 | ~$0.30 |
| Llama 3.3 70B | $0.75 | $0.99 |
| DeepSeek R1 70B | $0.75 | $0.99 |
| Kimi K2 | $1.50 | ~$1.50 |
Batch API: 50% discount on all models. Submit up to 50,000 requests in a single JSONL file (200MB max). Processing window: 24 hours to 7 days. Results stored securely for up to 30 days. Separate rate limits – batch jobs don’t consume your real-time quota.
Prompt caching: Additional 50% reduction on cached input tokens.
Access tiers:
- Free – no credit card required. Low rate limits for testing.
- Developer – self-serve signup. Up to 10x rate limits, Batch API access, 25% cost discount.
- Enterprise – custom contracts, dedicated instances, custom performance guarantees, LoRA fine-tunes.
The API is OpenAI-compatible. Switching from OpenAI requires changing approximately three lines of code – the base URL, the API key, and the model name.
Available Models
GroqCloud hosts open-weight models optimized for their LPU architecture:
- Llama 3.3 70B Versatile – general-purpose, best balance of quality and speed
- Llama 3.1 8B Instant – ultra-fast for simple tasks
- Llama 4 Scout – used in Groq’s Compound inference system
- DeepSeek-R1-Distill-Llama-70B – reasoning model with 128K context
- Qwen QwQ 32B – reasoning model
- GPT-OSS-120B – large open-source model
- Whisper Large v3 – audio transcription
- Various vision and text-to-speech models
Note: Groq does not host proprietary models (Claude, GPT-4, Gemini). You get open-weight models running at speeds that make them competitive with – or superior to – proprietary models for many structured tasks.
Compound Inference: Multi-Model Orchestration
Groq’s Compound system is a server-side agent that orchestrates multiple models and tools in a single API call:
- Uses Llama 4 Scout for core reasoning and Llama 3.3 70B for routing and tool selection
- Integrates web search, code execution, and other tools server-side
- All tool calls run on Groq’s inference fleet – no client-side orchestration needed
- The model iteratively consumes tool outputs, refines reasoning, and returns a polished answer
- A single API call replaces what would normally require a client-side agent loop
For report generation, this means: send a prompt like “analyze this company’s competitive position” and Compound handles the research, reasoning, and synthesis server-side at LPU speed.
Why This Matters for Bulk Report Generation
If you need to generate 50 analysis reports, the math changes dramatically with Groq:
Time comparison (Llama 70B-class model, ~2,000 token output per report):
| Provider | Tokens/sec | Time per report | 50 reports |
|---|---|---|---|
| GPU-based (typical) | 150 TPS | ~13 seconds | ~11 minutes |
| Groq | 800 TPS | ~2.5 seconds | ~2 minutes |
| Groq Batch API | Async | Submitted once | Results in hours |
Cost comparison (50 reports, ~1,500 input + 2,000 output tokens each):
| Provider | Input cost | Output cost | Total |
|---|---|---|---|
| OpenAI GPT-4o | $0.19 | $0.60 | $0.79 |
| Anthropic Claude Sonnet | $0.23 | $0.75 | $0.98 |
| Groq Llama 70B (real-time) | $0.06 | $0.10 | $0.16 |
| Groq Llama 70B (batch) | $0.03 | $0.05 | $0.08 |
Groq is roughly 5-10x cheaper than proprietary models and 4-7x faster. For structured analysis tasks – company research, market comparisons, partnership assessments, competitive analysis – an open-weight 70B model running at 800 tokens/second produces quality that is indistinguishable from proprietary models at a fraction of the cost and time.
The Practical Workflow
For someone generating lots of analysis reports, here is the setup:
For real-time, one-at-a-time reports:
- Sign up at console.groq.com (free tier, no credit card)
- Get an API key
- Use Llama 3.3 70B Versatile for quality analysis
- Call the API with your prompt template + variables per report
- Receive complete reports in 2-3 seconds each
For bulk batch processing:
- Upgrade to Developer tier
- Prepare a JSONL file with one request per line (up to 50,000 requests)
- Submit via the Batch API endpoint
- Results returned within 24 hours at 50% cost discount
- Download results as JSONL
For research-intensive reports (requiring web search):
- Use the Compound inference endpoint
- Single API call per report – Compound handles research + reasoning + synthesis
- No client-side agent orchestration needed
The Competitive Landscape
Groq is not the only fast inference provider:
- Cerebras – custom wafer-scale silicon, slightly faster than Groq on some benchmarks, but smaller model selection and less mature API
- Together.ai – GPU-optimized, higher throughput on large batches, competitive pricing
- SiliconFlow – claims 2.3x faster than leading GPU clouds
- Fireworks AI – GPU-based with heavy optimization, good balance of speed and model selection
Groq leads on latency (time-to-first-token and single-stream speed). Together.ai leads on throughput (total tokens across many concurrent requests). No provider wins on all three axes of speed, cost, and throughput simultaneously.
For the use case of generating analysis reports – where you care about per-report completion time and cost – Groq’s latency advantage is the most relevant metric.
The Bottom Line
GPUs are training machines pressed into inference service. The LPU is an inference machine built from first principles. The 24x memory bandwidth advantage translates directly into 4-7x faster text generation at competitive or lower cost.
For generating analysis reports at volume, the practical impact is: what takes 11 minutes on a GPU provider takes 2 minutes on Groq. What costs $1 on a proprietary model costs $0.08-0.16 on Groq’s Batch API. The quality difference between Llama 70B and proprietary models on structured analysis tasks is negligible.
The NVIDIA acquisition validates the architecture. GroqCloud remains operational. The Batch API exists specifically for high-volume workloads. The API is OpenAI-compatible. Switching is three lines of code.
If you are tired of waiting for answers to well-defined questions, the wait is a hardware problem. Groq solved it.
Sources:
- Groq Pricing
- Groq LPU Architecture
- Inside the LPU: Deconstructing Groq’s Speed
- Groq Batch API Documentation
- Groq Supported Models
- Groq Compound Inference
- Groq Enterprise Access
- GroqCloud Developer Tier
- NVIDIA-Groq Licensing Agreement
- NVIDIA Buying Groq for $20B – CNBC
- OpenAI Discontent with NVIDIA GPUs for Inference – TrendForce
- Fastest LLM Inference in 2026 – Yotta Labs
- Groq Inference Tokenomics – SemiAnalysis
- ArtificialAnalysis.ai Provider Benchmarks
- Groq Deterministic Architecture – Medium