Groq Review: The Fastest Inference Engine Money Can Buy

There is fast inference, and then there is Groq. The company's Language Processing Units - custom ASICs designed from the ground up for transformer inference - deliver output speeds that redefine what "real-time" means for LLM applications. When Groq first demonstrated Llama 2 70B at 300 tokens per second in early 2024, it felt like a stunt. Two years later, Groq is serving Llama 4 Maverick at over 1,200 tokens per second, has expanded to a 14-model lineup, processes over 1 billion API calls per month, and has become the default inference backend for latency-sensitive applications that cannot wait for GPU-based providers. After eight weeks of running production workloads on Groq, we can confirm: the speed is real, and it changes how you design applications.

The LPU Difference

Groq's LPU (Language Processing Unit) is a deterministic, synchronous processor designed specifically for sequential token generation. Unlike GPUs, which are optimized for parallel matrix multiplications during training, LPUs are optimized for the serial, memory-bandwidth-bound workload of inference. The key technical advantage is deterministic latency - every token takes the same amount of time to generate, which means no variance, no tail latency spikes, and predictable performance under load.

The hardware runs in Groq's own data centers. You do not buy or rent LPUs. You access them through Groq's API (OpenAI-compatible format) and pay per token. The infrastructure is entirely managed.

Speed Benchmarks

We measured output token throughput (tokens per second), time-to-first-token (TTFT), and total response time across Groq's model lineup and compared against GPU-based alternatives. All measurements are median values across 500 requests with a standard prompt.

The numbers speak for themselves. Groq is 4-7x faster on output throughput and 3-4x faster on time-to-first-token compared to the fastest GPU-based inference providers. On the smaller Llama 4 Scout model, Groq sustains 1,580 tokens per second - fast enough that a 500-token response completes in roughly 300 milliseconds total. At that speed, LLM responses feel instantaneous.

What Speed Enables

The obvious benefit is user experience. Chat applications on Groq feel qualitatively different from GPU-based inference. Responses appear to materialize rather than stream. For voice applications where latency determines whether conversation feels natural, Groq's sub-100ms TTFT is transformative.

But the deeper impact is architectural. When inference is this fast, you can afford to make multiple LLM calls where you would previously make one. A retrieve-then-generate pipeline that previously took 3 seconds can complete in under 500ms. A chain of three LLM calls - classify, retrieve, generate - that was too slow for interactive use at GPU speeds becomes viable at Groq speeds. Agent loops that make dozens of tool-use calls per interaction become responsive rather than tedious.

We tested this with a multi-step research agent. On GPU-based inference (Together AI with Llama 4 Maverick), a 5-step agent loop averaged 12.4 seconds. On Groq with the same model, the same loop averaged 2.1 seconds. The agent did not get smarter; it just stopped making the user wait.

Model Availability

Groq's model lineup has expanded significantly but remains limited compared to the full LLM landscape. The current roster includes:

• Llama 4 family (Maverick, Scout)
• Llama 3.3 (70B, 8B)
• Mixtral 8x7B
• Gemma 3 (27B, 9B)
• Qwen 3 (32B)
• Whisper Large V3 (speech-to-text)
• Llama Guard 3 (content moderation)

Notably absent: any closed-source model. You cannot run Claude, GPT-5, or Gemini on Groq. The service is exclusively for open-source and open-weight models that can be deployed on LPU hardware. This is a fundamental architectural constraint - LPU chips run specific model architectures, and adding a new model requires hardware-level optimization.

For applications that can use open-source models, this is not a limitation. Llama 4 Maverick is competitive with Claude Sonnet and GPT-5.2 mini on most benchmarks. But for applications that require frontier model capabilities - the kind where only Opus, GPT-5.2, or Gemini 3.1 Pro will do - Groq is not an option.

Pricing

Groq's pricing is competitive with GPU-based alternatives and sometimes cheaper, despite the speed advantage:

The free tier offers 30 requests per minute with generous daily token limits - enough for development and light production use. Paid plans start at $0.99/month (higher rate limits) and scale to enterprise agreements with dedicated capacity and SLA guarantees.

The pricing advantage exists because LPUs are more efficient per token than GPUs for inference workloads. Groq is not subsidizing prices; the hardware genuinely costs less to operate per output token.

Reliability

In eight weeks of production use, we observed 99.94% uptime - three brief outages totaling 32 minutes. Error rates averaged 0.12% of requests, predominantly 429 (rate limit) responses during peak hours.

The deterministic latency is the reliability story. GPU-based inference has tail latency problems - the p99 can be 5-10x the median. Groq's p99 is within 15% of the median because the hardware produces deterministic timing. For applications with strict latency SLAs, this predictability is as valuable as the raw speed.

Rate limiting during peak hours is the primary reliability concern. Groq's capacity is physically constrained by the number of LPU chips deployed. During US business hours, we occasionally hit rate limits on popular models despite being on a paid plan. Groq has been expanding capacity quarterly, but demand consistently outpaces supply.

Strengths and Weaknesses

Strengths:

• 4-7x faster output throughput than GPU-based alternatives
• Sub-100ms time-to-first-token for most models
• Deterministic latency with minimal p99/p50 variance
• Competitive or cheaper pricing than GPU-based providers
• Generous free tier for development
• OpenAI-compatible API for easy integration
• Whisper support for speech-to-text at similarly fast speeds
• Speed enables new multi-call architectures

Weaknesses:

• Open-source models only - no Claude, GPT, or Gemini
• Limited model lineup (14 models) compared to GPU-based providers
• Rate limiting during peak hours on popular models
• No fine-tuned model support
• No on-premise deployment option
• Adding new models requires hardware-level optimization
• Maximum context window limited by LPU memory (currently 128K)

Verdict: 8.8/10

Groq is not an incremental improvement in inference speed. It is a step function. The 4-7x throughput advantage and sub-100ms TTFT transform what is architecturally possible with LLM applications. Multi-call agent loops become responsive. Voice applications become natural. Streaming becomes unnecessary because complete responses arrive faster than you can read them.

The limitation is model availability. If your application needs Claude Opus or GPT-5.2, Groq cannot help. But if your application can use Llama 4 Maverick, Mixtral, or Gemma 3 - and for many applications these models are more than sufficient - Groq delivers those models faster and cheaper than anyone else.

The real question Groq poses to the industry is whether speed is a feature or a platform. Today it is a feature: faster inference on the same models. But as LLM applications become more agentic, more multi-step, and more latency-sensitive, speed becomes the difference between architectures that are possible and architectures that are practical. Groq is betting that the future of AI is not just smarter models but faster ones, and the bet is looking increasingly correct.

Sources:

• Groq Official Site - Groq
• Groq API Documentation - Groq
• Groq LPU Architecture Deep Dive - Groq
• Groq Processes 1 Billion Monthly API Calls - VentureBeat
• LPU vs GPU: Inference Benchmark Comparison - Anyscale