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Groq vs Inference.net
Inference.net specializes in cheap batch on spare GPUs and distilling custom models. Groq specializes in fast real-time inference. Opposite ends of the latency scale.
By The Subconscious Team · Updated
Groq vs Inference.net: key differences
Inference.net was built on idle GPU time. Its Batch API takes up to 1M requests per file with completion windows of 24 hours to 7 days, and it admits that fragmented spare capacity suits batch better than strict real-time SLAs. Groq exists for real-time. Its LPU returns tokens at several times GPU speeds and holds tail latency close to the median. These two rarely compete for the same workload. An app could run nightly classification on Inference.net and live voice turns on Groq.
Customization is Inference.net's other card. Its gateway captures production traffic, turns it into datasets and fine-tunes a smaller task-specific model, then serves it on a dedicated GPU with a 99.99% uptime target. Groq hosts no fine-tuned models. Groq has its own batch discount for cheap offline work on its catalog, but its catalog is small. Inference.net has few independent benchmarks, so buyers depend on its numbers, while Groq publishes its per-model speeds.
What Groq and Inference.net do
Groq
Groq serves open models on its own chip, the LPU, which keeps model weights in on-chip SRAM and runs a deterministic schedule instead of waiting on GPU memory. Groq publishes 1,000 tokens per second on GPT-OSS 20B and 500 on GPT-OSS 120B. Latency stays tight between median and tail, which matters for strict SLAs. The API is OpenAI-compatible and also hosts Whisper for speech to text plus an agentic system called Groq Compound with built-in search and code execution.
Example models: GPT-OSS 120B, Qwen 3.6 27B
Full Groq profileInference.net
Inference.net started as a buyer of last resort for idle GPU time. Its scheduler aggregates small unused chunks of capacity across data centers and runs models on them, and it passes the steep discounts it gets from those data centers on to customers. That origin still shows in its OpenAI-compatible Batch API, which takes up to 1M requests per file with completion windows from 24 hours to 7 days and far higher headroom than synchronous limits.
Example models: open catalog models plus customer fine-tunes served on dedicated GPUs
Full Inference.net profileShould you choose Groq or Inference.net?
Groq
Choose Groq for
- Live voice and chat turns
- Latency-bound steps inside agent loops
- Fast speech to text with Whisper
Inference.net
Choose Inference.net for
- Million-request offline jobs over multi-day windows
- Distilling traces into a custom small model
- Routing open, closed and custom models under one key
Groq vs Inference.net at a glance
| Attribute | Groq | Inference.net |
|---|---|---|
| Model access | Open weights | Open, closed and custom |
| Flagship models | GPT-OSS 120B, Qwen 3.6 27B | Customer fine-tunes |
| Speed | 500–1,000 tok/s | Batch windows of 24h to 7 days |
| Price | Near the floor on small models | Discounted spare GPU capacity |
| Customization | No fine-tuned model hosting | Distill traces into custom models |
| Deployment | GroqCloud API | Batch API, gateway, dedicated GPUs |
| Long context | Around 131K max | Varies by model |
Frequently asked questions
What is the difference between Groq and Inference.net?
Inference.net specializes in cheap batch on spare GPUs and distilling custom models. Groq specializes in fast real-time inference. Opposite ends of the latency scale.
When should I choose Groq over Inference.net?
Live voice and chat turns; Latency-bound steps inside agent loops; Fast speech to text with Whisper.
When should I choose Inference.net over Groq?
Million-request offline jobs over multi-day windows; Distilling traces into a custom small model; Routing open, closed and custom models under one key.
Is Groq or Inference.net cheaper?
Groq: Near the floor on small models. Inference.net: Discounted spare GPU capacity. The cheaper choice depends on the model and workload.
Which has more context, Groq or Inference.net?
Groq: Around 131K max. Inference.net: Varies by model.
Related comparisons
Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.