Groq Alternatives for Fast LLM Inference in 2026

The best Groq alternatives depend on why you're leaving. If you want raw speed on custom silicon, try Cerebras or SambaNova. If you want the widest catalog of open models on GPUs, try Together AI, Fireworks AI or DeepInfra. If you want to stop betting on one provider, put OpenRouter in front of several. If you want full control, self-host with vLLM.

Groq itself hasn't gone anywhere. GroqCloud still runs, and the company raised new money in 2026. What changed is the menu. Through 2026, Groq retired most of the Llama and Qwen models that free and developer-tier users relied on, and pointed them at OpenAI's open-weight gpt-oss models instead. If your app was built on Llama 3.3 70B at Groq speed, that's the reason you're reading this.

Below: what Groq is in 2026, a quick comparison, then each alternative with its trade-off.

What Groq Is in 2026

There are two stories here, and people mix them up.

The Nvidia deal. On December 24, 2025, Groq announced a non-exclusive licensing agreement with Nvidia for its inference technology. Founder Jonathan Ross, President Sunny Madra and other team members joined Nvidia. Simon Edwards became Groq's CEO. Groq says it continues as an independent company and that GroqCloud would "continue to operate without interruption."

So it was a licence plus a team move, not an acquisition of the company. Press reports put a dollar figure on it; Groq's own announcement doesn't.

The company after the deal. In August 2026, Groq announced a $350 million round and described itself as an Nvidia Cloud Partner, with plans to grow its data center capacity in 2027. Its homepage now calls Groq a "neocloud for fast inference" and says its LPU work runs alongside Nvidia GPUs.

The model list. This is the part that affects developers. Groq's deprecations page shows a steady run of retirements in 2026:

For the Llama and Qwen retirements, Groq notes they apply to free and developer-tier usage, and enterprise customers with a committed-spend contract aren't affected. Today the self-serve model list is short: gpt-oss-120b and gpt-oss-20b, Whisper for speech-to-text, a Qwen 27B model in preview, and a few safety and voice models.

Where Groq is still the right call: you're happy on gpt-oss, you want very high tokens per second, and you like an API that's mostly compatible with OpenAI's client libraries. Groq's free and developer tiers are still there for that.

Quick Comparison

Specialty Chips: The Closest Match to Groq's Speed

Groq's pitch was always custom hardware that generates tokens faster than GPUs. Two other companies sell the same idea.

Cerebras: the most direct Groq alternative

Cerebras builds wafer-scale chips and runs them as an inference cloud. Its homepage leads with speed, and it offers "drop-in OpenAI API compatibility," so switching from Groq is mostly a base URL and a key.

What stands out:

• Bigger models at speed. The homepage lists models such as Kimi K3.1 and Qwen 3.8 27B, and says the lineup is updated regularly. That's a different menu from Groq's current self-serve list.
• Big-name buyers. OpenAI's head of compute infrastructure is quoted on the homepage saying Cerebras adds dedicated low-latency inference to OpenAI's platform.
• More ways in. The pricing page says Cerebras inference is also offered through AWS Marketplace, OpenRouter, Hugging Face and Vercel.

The trade-off: the catalog is small and curated, like Groq's. If the exact model you want isn't on the list, speed doesn't help. Start-up access is a small one-time trial credit, not an ongoing free tier.

SambaNova: fast inference on large open models

SambaNova designs its own RDU chips and offers them through SambaCloud, a developer API that it says is OpenAI compatible. It adds new open models regularly; in October 2026 it announced MiniMax M3.1 Flash on SambaCloud.

SambaNova also sells hardware to neoclouds, governments and enterprises, so the developer cloud is one part of a larger business.

Pick it if you want custom-silicon speed on a large open model that Groq doesn't serve self-serve. Skip it if you need a long catalog or lots of small models; like Cerebras, the menu is curated.

GPU Clouds: When You Need More Models

If Groq's problem for you is model choice, these providers trade a little speed for a much bigger menu. All three run on Nvidia GPUs. We compared them from a different angle, by billing model, in our Replicate alternatives guide.

Together AI: widest catalog plus training

Together AI calls itself "The AI Native Cloud." It offers serverless inference for open models, dedicated single-tenant endpoints, provisioned throughput with an uptime SLA, fine-tuning, and GPU clusters you can rent by the hour.

Pricing follows the product: per million tokens for serverless, per GPU per hour for dedicated inference and clusters, and per token processed for fine-tuning.

Pick it if you want to try many open models from one account, then fine-tune one and move it to dedicated capacity without changing vendors. It won't match Cerebras or Groq on raw tokens per second for most models.

Fireworks AI: production serving with cost levers

Fireworks AI is built for teams running open models in production. Its pricing page shows how many ways it lets you trade speed against cost:

• Serverless per token, with Standard, Priority, Fast, Batch and Spot modes
• Batch jobs at 50% off input and output
• Spot at about 30% off on best-effort capacity, where throughput can drop to zero
• On-demand GPUs billed per GPU-second, which scale to zero after an hour idle
• Training, priced per training token or per GPU-hour

Pick it if you have a mix of real-time and background work and want to pay less for the background part. The menu of modes takes some learning compared with Groq's single fast endpoint.

DeepInfra: low-cost per-token access

DeepInfra is a plain pay-as-you-go inference provider. Its docs say it offers an OpenAI-compatible chat completions API for all its LLMs, so switching takes three changes: base URL, API key and model name. It also rents GPU instances if you outgrow shared endpoints.

Pick it if you want many open models at low per-token prices with no contract. It's less of a platform than Together or Fireworks, with fewer tools around training and deployment.

Routers and Self-Hosting: Stop Depending on One Provider

The Groq retirements are a good reminder that a provider can drop the model your app depends on. These two options reduce that risk.

OpenRouter: one API, many providers

OpenRouter sits in front of many providers, including fast ones. You can sort providers by throughput or latency, or add :nitro to a model slug to prefer the fastest endpoint. If one provider retires a model, another may still serve it.

Pick it if you want speed without committing to one vendor's catalog. You add a layer between you and the provider, and you don't control which hardware serves a given request unless you pin it. If you're already on OpenRouter and want out, see our OpenRouter alternatives.

vLLM: run the model yourself

vLLM is an open-source serving engine under the Apache-2.0 licence, with an OpenAI-compatible API server built in. The repo is very active. Point it at a GPU and an open model, and you get an endpoint nobody can deprecate except you.

The trade-off: you own the GPUs, the scaling, the uptime and the bill. On a single GPU you won't come close to Groq's tokens per second for large models. For the difference between vLLM and a local runner, read vLLM vs Ollama.

Pick it if you need a specific model long term, have privacy rules that keep data in your own environment, or run enough traffic that renting GPUs beats paying per token.

How to Choose a Groq Alternative

Start with one question: what actually broke for you?

A few checks before you switch:

1. Test the model, not the provider. The same open model can behave slightly differently across providers. Run your own prompts and compare output, not just speed.
2. Measure time to first token and tokens per second on your real prompts. Vendor speed claims come from their own tests.
3. Read the deprecation page. Every provider here retires models eventually. Know where the notice goes and how much warning you get.
4. Keep your code OpenAI-compatible. Groq, Cerebras, SambaNova, DeepInfra and vLLM all speak an OpenAI-style API, so switching later is cheap if you don't lock into provider-only features.
5. Ask about enterprise terms. Groq's own notes say committed-spend customers kept the retired Llama and Qwen models. If you have real volume, that conversation may be cheaper than migrating.

Where Taku Fits

Taku isn't an inference API, so it doesn't replace Groq or any provider here. It solves a different problem: you've seen someone share a working AI app or agent built on these models, and you can't get the repo, keys and setup running yourself.

Taku is an AI-native desktop workspace where you can mirror a working AI app or workflow someone shared, remix it for your own work, and keep it instead of re-prompting from scratch. Taku is in Beta, and the Mac app is available now.

FAQ

What is the best Groq alternative?

For speed, Cerebras is the closest match: custom chips, an OpenAI-compatible API, and larger open models than Groq's current self-serve list. For model choice, Together AI or DeepInfra. For long-term control, self-host with vLLM.

Is Groq shutting down?

No. Groq says it remains an independent company and GroqCloud keeps running. In December 2025 it licensed its inference technology to Nvidia, and its founder and president joined Nvidia. In August 2026 it announced a $350 million funding round.

Did Nvidia buy Groq?

Not according to Groq. Its announcement describes a non-exclusive licensing agreement for Groq's inference technology, with some team members joining Nvidia. Groq kept operating under a new CEO, Simon Edwards.

Why can't I use Llama 3.3 70B on Groq anymore?

Groq retired Llama 3.3 70B and Llama 3.1 8B for free and developer-tier usage on August 16, 2026, and recommends gpt-oss-120b or gpt-oss-20b instead. Enterprise customers with a committed-spend contract weren't affected.

Which Groq alternatives have a free tier?

Groq still has one with rate limits. Cerebras offers a small one-time trial credit. Together AI, Fireworks AI and DeepInfra are built around pay-as-you-go billing rather than a free tier. Self-hosting vLLM is free software, but the GPUs aren't.

Is Cerebras faster than Groq?

Both sell speed as their main feature and both publish their own benchmarks. Results depend on the model and the prompt length, so test your own workload on both before deciding.