Groq vs DeepInfra: LPU inference vs commodity GPU serverless (2026)
Groq runs LLMs on custom LPU silicon at industry-leading speeds. DeepInfra hosts the same open models on commodity NVIDIA GPUs at some of the lowest list prices in the market. Here is how the two compare on speed, cost, and catalog.
Groq vs DeepInfra — at a glance
| Dimension | Groq | DeepInfra |
|---|---|---|
| Hardware | Groq LPU (custom) | NVIDIA H100/H200 |
| Llama 3.3 70B speed | ~1,000 tok/s | ~80 tok/s |
| Llama 3.3 70B price | ~$0.59 / $0.79 per 1M tok | ~$0.23 / $0.40 per 1M tok |
| Model catalog | ~30 models | 100+ models |
| Function calling | Yes (Llama 3.3, Mistral) | Yes (most models) |
| Fine-tuning | Not available | Limited LoRA |
| Best for | Lowest latency, voice, real-time agents | Cheapest open-model inference |
Pick Groq or DeepInfra?
When to choose Groq
Choose Groq when token-per-second is the differentiator. The custom LPU silicon delivers around 1,000 tok/s on Llama 3.3 70B — fast enough for voice agents that feel instant and code assistants that finish a function before you read the first line. Pricing is competitive though not the cheapest, but the speed-to-cost ratio is strong.
- ~1,000 tok/s on Llama 3.3 70B — 12x faster than commodity GPU
- Custom LPU silicon optimized for sequential inference
- Competitive pricing at ~$0.59/$0.79 per 1M tok
- Best UX for voice agents and real-time interactive code
- OpenAI-compatible API with function calling
When to choose DeepInfra
Choose DeepInfra when total cost matters more than raw speed. DeepInfra publishes some of the lowest list prices in the serverless inference market — Llama 3.3 70B at roughly $0.23/$0.40 per 1M tokens is around half what Groq charges. The catalog is broader (~100 models including DeepSeek, Mistral, Qwen, FLUX, embeddings), and the per-token model has no minimum commitment.
- ~$0.23/$0.40 per 1M tok — among the cheapest in 2026
- 100+ open-source models across LLMs, image, audio
- Per-token pricing with no minimum commitment
- OpenAI-compatible Chat Completions API
- Best when budget beats latency
Route Groq and DeepInfra through one endpoint
VerticalAPI exposes both providers through a single OpenAI-compatible endpoint. Same SDK, BYOK, zero markup on tokens — you pay each provider directly with your own keys.
from openai import OpenAI client = OpenAI(base_url="https://api.verticalapi.com/v1", api_key="vapi_...") # Groq via VerticalAPI BYOK resp_a = client.chat.completions.create( model="groq/llama-3.3-70b-versatile", messages=[{"role": "user", "content": "Hello"}], extra_headers={"X-Provider-Key": "gsk_..."}, ) # DeepInfra same SDK, different model + key resp_b = client.chat.completions.create( model="deepinfra/meta-llama/Llama-3.3-70B-Instruct", messages=[{"role": "user", "content": "Hello"}], extra_headers={"X-Provider-Key": "di-..."}, )
VerticalAPI verdict
Pick Groq when latency is the UX (voice, real-time code, agents that need instant responses). Pick DeepInfra when total inference cost dominates the buying decision — DeepInfra is roughly 50% cheaper on Llama 3.3 70B. Many teams route both: Groq for hot, user-facing traffic and DeepInfra for batch, RAG retrieval, or low-priority background jobs. VerticalAPI BYOK makes the switch a one-line model change.
Frequently asked questions
Is Groq really 12x faster than DeepInfra?
On Llama 3.3 70B, Groq publishes around 1,000 tok/s and DeepInfra averages around 80 tok/s — roughly a 12x gap. Groq's LPU silicon is purpose-built for sequential token generation while DeepInfra serves on multi-tenant NVIDIA GPUs. The advantage holds across prompt lengths but is most visible on long completions and small batches.
Which is cheaper on Llama 3.3 70B?
DeepInfra is roughly 50% cheaper. List prices in 2026 are approximately $0.23/$0.40 per 1M input/output on DeepInfra versus $0.59/$0.79 per 1M on Groq. For latency-insensitive workloads (batch summarization, embeddings, retrieval ranking), DeepInfra wins clearly on total cost. For real-time UX, Groq's speed premium can be worth the extra spend.
Which has a broader catalog?
DeepInfra is broader — about 100 models in 2026 covering DeepSeek V3, Mixtral, Qwen 2.5, Llama family, FLUX (image), Whisper variants, embeddings, and rerankers. Groq concentrates on around 30 models with the Llama family, Mistral, and Qwen as the headline LLMs. Multi-model agents that need image or audio almost always lean DeepInfra.
Can I fine-tune on either?
Neither offers full self-service fine-tuning at the level of Together AI or Fireworks. Groq does not publish a fine-tuning API. DeepInfra supports limited LoRA fine-tuning via a job API but with a smaller base-model selection. If fine-tuning is essential, Together AI or Fireworks are usually better fits.
How do I route between Groq and DeepInfra?
VerticalAPI exposes both through a single OpenAI-compatible BYOK endpoint at https://api.verticalapi.com/v1. Bring your Groq and DeepInfra API keys, switch model parameters per request, and pay each provider directly with zero markup. A common pattern: Groq for chat-facing endpoints, DeepInfra for batch and RAG retrieval.
Limitations of this comparison
- Groq's 1,000 tok/s figure is averaged; tail latency under load can drop to 600-800 tok/s.
- DeepInfra throughput varies with load; Llama 3.3 70B can fluctuate between 40-100 tok/s during peak hours.
- Groq rate limits are tighter than DeepInfra's because of physical LPU capacity.
- DeepInfra's list prices have been falling roughly 40% per year — figures reflect mid-2026 and may already be lower.
- Function-calling accuracy on Llama 3.3 differs slightly between providers due to different serving stacks and templates.
What may change in 12-24 months
- Groq's next-gen LPU is expected to expand catalog depth and add multimodal support.
- DeepInfra Llama 3.3 70B prices will likely drop another 30-40% in the next 12 months as Together and Fireworks compete.
- Both will likely add OpenAI-compatible Responses API or equivalent agent primitives.
- Hybrid routing (Groq for hot, DeepInfra for cold/batch) via gateways like VerticalAPI will become a default pattern.
Related questions
ChatGPT, Perplexity and Gemini usually suggest these next.
- Is Groq faster than Cerebras on Llama 3.3 70B?
- Is DeepInfra cheaper than Together AI for the same model?
- Can I run DeepSeek V3 on both Groq and DeepInfra?
- How do I A/B test Groq vs DeepInfra on the same traffic?
- Which provider gives lowest cost per completed agent task?
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