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Groq vs Hugging Face: Token Pricing, Speed & Intelligence

Full comparison of Groq and Hugging Face — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.

Groq

LPU + LPX inference — high-throughput tokens per second, $350M Series A

Groq runs custom Language Processing Units (LPUs) and the newer LPX chips that work alongside NVIDIA next-generation GPUs for high-throughput inference. Groq raised a $350M Series A in 2026 and is building hundreds of megawatts of capacity. Check listed rates and provider documentation for current model availability and throughput figures.

SpeedReal-time chatVoice AICost-efficiencyBatch processing
Open-weight hostHosts open weights

Hugging Face

Serverless Inference API — 150K+ open models including Llama, Qwen, and FLUX

Hugging Face is the central hub for open-source AI models, hosting 150K+ models across text, image, audio, and video. Its Serverless Inference API provides pay-per-request access to popular open-weight models including Llama 3.3 70B, Qwen 2.5 72B, and FLUX.1-dev — without managing infrastructure. Ideal for developers who want open-weights models without self-hosting.

Open-model inferencePrototypingImage generationResearchFine-tuned model hosting
Open-weight hostHosts open weights

Key metric comparison

Groq
Hugging Face
Cheapest input ($/1M)

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Cheapest output ($/1M)

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Peak throughput

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Best latency (TTFT)

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Intelligence score

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Context window

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Live token pricing

Strengths and weaknesses

Groq

750+ tokens/sec on Llama 3.3 70B — high-throughput GPU-class inference (Groq-published)
Sub-100ms time-to-first-token for real-time applications
LPX chips alongside LPUs for expanded capacity
Very competitive pricing on open-weight models
OpenAI-compatible API
Free tier available
Limited model selection vs. Together AI or Fireworks
No vision model support on most models
No fine-tuning capability

Hugging Face

Largest open-model hub — 150K+ models across all modalities
Serverless Inference API requires no infrastructure management
Access to Llama, Qwen, Mistral, FLUX, and hundreds of other open-weight models
Free tier available for low-volume experimentation
Deep ecosystem: datasets, Spaces, model cards, and community fine-tunes
Serverless API has cold-start latency for less popular models
No proprietary frontier models — all hosted models are open-weight
Rate limits on free tier; dedicated endpoints required for production SLAs
Pricing varies by model and can be less predictable than fixed-rate APIs

Key differentiators

Groq

Groq's custom LPU/LPX chips deliver high-throughput inference significantly faster than conventional GPU systems, backed by $350M in Series A funding and hundreds of megawatts of planned capacity.

Hugging Face

The world's largest open-model hub — Serverless Inference API gives instant pay-per-request access to Llama, Qwen, FLUX, and 150K+ other open-weight models without any infrastructure setup.

Frequently asked questions

Groq FAQs

How fast is Groq inference?

Groq delivers 750+ tokens/second on Llama 3.3 70B and 1,200+ tokens/second on Llama 3.1 8B using custom LPU/LPX chips, making it well-suited for real-time and low-latency applications.

What is Groq's LPX chip?

The LPX is Groq's next-generation inference chip, introduced alongside the existing LPU lineup. It expands Groq's capacity and enables even higher throughput for large-scale deployments.

How much does Groq cost?

Groq uses per-token pricing across its model catalog. Check console.groq.com/docs/openai for current rates on Llama 3.3 70B, Llama 3.1 8B, and other models.

Hugging Face FAQs

What is the Hugging Face Inference API?

The Hugging Face Serverless Inference API provides pay-per-request access to popular open-weight models including Llama 3.3 70B, Qwen 2.5 72B, and FLUX.1-dev. No infrastructure management required — just an API key.

How much does the Hugging Face API cost?

Serverless Inference API pricing varies by model. Llama 3.3 70B and Qwen 2.5 72B are $0.59/$0.79 per 1M input/output tokens. FLUX.1-dev is $0.025 per image. A free tier is available for low-volume use. See huggingface.co/pricing for current rates.

Can I use my own fine-tuned model on Hugging Face?

Yes. You can deploy your own fine-tuned model on Hugging Face Inference Endpoints (dedicated) or upload it to the Hub for Serverless Inference if it meets the supported architecture requirements.

Provider resources

Groq

Provider page
Website
PricingDocs

Hugging Face

Provider page
Website
PricingDocs

Groq — LPU + LPX inference — high-throughput tokens per second, $350M Series A

Groq runs custom Language Processing Units (LPUs) and the newer LPX chips that work alongside NVIDIA next-generation GPUs for high-throughput inference. Groq raised a $350M Series A in 2026 and is building hundreds of megawatts of capacity. Check listed rates and provider documentation for current model availability and throughput figures.

Groq's custom LPU/LPX chips deliver high-throughput inference significantly faster than conventional GPU systems, backed by $350M in Series A funding and hundreds of megawatts of planned capacity.

Hugging Face — Serverless Inference API — 150K+ open models including Llama, Qwen, and FLUX

Hugging Face is the central hub for open-source AI models, hosting 150K+ models across text, image, audio, and video. Its Serverless Inference API provides pay-per-request access to popular open-weight models including Llama 3.3 70B, Qwen 2.5 72B, and FLUX.1-dev — without managing infrastructure. Ideal for developers who want open-weights models without self-hosting.

The world's largest open-model hub — Serverless Inference API gives instant pay-per-request access to Llama, Qwen, FLUX, and 150K+ other open-weight models without any infrastructure setup.

Key strengths compared

Groq

  • 750+ tokens/sec on Llama 3.3 70B — high-throughput GPU-class inference (Groq-published)
  • Sub-100ms time-to-first-token for real-time applications
  • LPX chips alongside LPUs for expanded capacity
  • Limited model selection vs. Together AI or Fireworks
  • No vision model support on most models

Hugging Face

  • Largest open-model hub — 150K+ models across all modalities
  • Serverless Inference API requires no infrastructure management
  • Access to Llama, Qwen, Mistral, FLUX, and hundreds of other open-weight models
  • Serverless API has cold-start latency for less popular models
  • No proprietary frontier models — all hosted models are open-weight

Key weaknesses compared

Groq

  • Limited model selection vs. Together AI or Fireworks
  • No vision model support on most models
  • No fine-tuning capability

Hugging Face

  • Serverless API has cold-start latency for less popular models
  • No proprietary frontier models — all hosted models are open-weight
  • Rate limits on free tier; dedicated endpoints required for production SLAs
  • Pricing varies by model and can be less predictable than fixed-rate APIs

Provider category context

Groq is a inference api, founded in 2016. Hugging Face is a open source host, founded in 2016. The category difference means these providers serve partially overlapping use cases — compare the model lists and pricing tables above to find the best fit for your specific workload.

How to choose between them

Choose Groq if you need 750+ tokens/sec on llama 3.3 70b — high-throughput gpu-class inference (groq-published). Choose Hugging Face if you need Open-model inference. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.