Groq Alternatives for Low-Latency LLM Inference

TL;DR

Groq is genuinely fast. Its LPU architecture is built for sequential token generation, and one benchmark reports sub-100ms Time to First Token and 800+ TPS on it. But "fastest" is not a single number. It splits into time-to-first-token (TTFT), sustained throughput, cost per million tokens, model coverage, and whether you can run your model and your pre/post-processing. The right Groq alternative is the one whose latency profile and cost envelope match the workload you actually ship.

This guide maps five brief-backed alternatives - Cerebras, SambaNova, Together AI, Fireworks AI and SiliconFlow - to the decision axes a VP of Engineering actually weighs, then covers migration and evaluation.

Why teams look past Groq

Groq's fixed-function LPU is optimized for one thing: fast decode on supported models. That is exactly what you want for streaming chat and voice. It is less of a fit when your constraints sit elsewhere:

• You need a large model (e.g. 405B-class) at high throughput, not just low single-stream latency.

• You run a fine-tuned or custom model that a hosted token API does not serve.

• Your bottleneck is cost per million tokens at scale, not milliseconds.

• You need to own the full request path - tokenizer, retrieval, guardrails, business logic - in your own container.

Those are different problems, and they point to different providers.

The metrics that decide it

Before comparing vendors, fix the metrics. We walk through these in depth in our guide to LLM serving goodput and latency SLOs, but briefly:

• TTFT vs throughput. TTFT governs how responsive a stream feels; throughput (tokens/sec) governs how fast a long answer or batch completes. A provider can win one and lose the other. In our benchmarks of serving frameworks on a single H100, vLLM gave the lowest TTFT at 123ms while SGLang won throughput at 460 tokens/sec on batch size 64 - two different winners on the same hardware.

• Cost per million tokens. The headline rate only matters alongside the latency you get for it.

• Model support. Does the provider serve the exact model (and fine-tune) you need?

• Hardware. LPU, wafer-scale, RDU or GPU each have a different throughput-per-dollar curve.

• End-to-end network latency. The token API is only one hop; see network latency in the AI inference pipeline.

As a reference point, one comparison clocks Groq at 0.14s TTFT with 275 tokens/sec on Distill-Llama-70B at $0.75/$0.99 per million tokens.

The alternatives compared

Cerebras - wafer-scale throughput

Cerebras runs on a Wafer-Scale Engine that it describes as 58x larger than GPUs and claims delivers up to 30x faster inference than GPU systems. On throughput specifically, one benchmark reports 2,900+ tokens per second on Llama models - 10-20x higher than GPU-based systems. Choose Cerebras when raw tokens-per-second on supported models is the product.

SambaNova - large models fast

SambaNova's edge is big models at speed. It reports over 100 tokens/second for Llama 3.1 405B where the average GPU provider delivers about 20, and up to 580 tokens/second on Llama 3.1 70B for agentic use cases. If your workload depends on the largest open models, SambaNova is the throughput-oriented alternative.

Together AI - broad catalog, low latency

Together AI leans on breadth. It serves 200+ open-source LLMs with sub-100ms latency at lower cost than proprietary solutions, and claims up to 2.75x faster inference than standard GPU providers with no infrastructure to manage. It's a strong default when you want selection plus fine-tuning. For adjacent options, see our Together AI alternatives for fine-tuned LLM inference.

Fireworks AI - fast engine, fast model availability

Fireworks AI's FireAttention engine reports 3 to 12x lower latency and up to 5.6x higher throughput than a self-hosted vLLM deployment, and the team targets availability of new open-source weights within 24 hours of release. Good for teams that want the newest open models quickly. We cover the trade-offs in Fireworks AI alternatives for open-source LLM inference.

SiliconFlow - cost-leaning speed

SiliconFlow built a proprietary engine that, per one 2026 benchmark, delivered up to 2.3x faster inference speeds and 32% lower latency than traditional cloud providers. Consider it when you want a speed bump without the premium of the headline hardware vendors.

At-a-glance comparison matrix

All figures are vendor- or third-party-published; validate against your own traffic.

Which to choose by use case

• Real-time chat / voice: prioritize TTFT - Groq, or Together AI's sub-100ms tier.

• Agentic loops on large models: SambaNova's 405B/70B throughput.

• Batch and offline generation: Cerebras wafer-scale throughput.

• Newest open-source model, day one: Fireworks AI's 24-hour target.

• Cost-sensitive general inference: SiliconFlow.

• Custom/fine-tuned model with your own request path: a serverless GPU platform where you ship your container and serve an OpenAI-compatible endpoint. This is where Cerebrium fits: in one case study, we measured 50% lower inference costs while increasing accuracy from 83% to 92% at production scale. When you control the container you also control cold starts - loading a TensorRT-LLM Llama 3 8B engine takes roughly ~10-15s on cold start in our own testing.

Migration & evaluation checklist

1. Define the SLO. TTFT target, p99 latency, and minimum throughput - separately.

2. Benchmark on your prompts. Vendor numbers use their inputs; our figures above came from 256 input tokens, 1xH100, batch size 1, total roundtrip to the platform. Replicate your real distribution.

3. Price at your volume. Multiply by actual monthly tokens; see LLM inference cost at scale.

4. Confirm model support. Check the exact model and any fine-tune are first-class.

5. Test cold starts and autoscaling under bursty traffic, not steady state.

6. Measure end-to-end, including network hops, not just the token API.

Bottom line

Groq is a great answer to one question - fast decode on supported models. If your question is different (biggest models, broadest catalog, lowest cost, or your own custom model and request path), one of these alternatives fits better. Pick on the metric that actually bounds your product, then prove it on your own traffic before you migrate.