vs
Groq vs Luminal
Groq gets speed from custom LPU chips on a small catalog. Luminal gets it from compiling models for the GPUs and ASICs you already have.
By The Subconscious Team · Updated
Groq vs Luminal: key differences
Groq built its own LPU chip and serves a short list of open models, like GPT-OSS 120B and Qwen 3.6 27B, at 500 to 1,000 tokens per second with tight tail latency. Context caps around 131K and there is no fine-tuned model hosting. Luminal takes the software route: its compiler turns any model into native kernels for GPUs or ASICs ahead of time, so speed comes without moving to new silicon.
Groq wins on per-request speed for the models it carries. Luminal's claim is aggregate throughput, 36K tokens per second on GPT-OSS 120B across 8 H100s, which matters more for batch-heavy serving than for one fast stream. Luminal also serves models Groq does not, including your own fine-tunes, though its cloud is early access and unpriced.
What Groq and Luminal 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 profileLuminal
Luminal builds an inference compiler. Where vLLM and SGLang interpret a model at runtime, Luminal compiles it ahead of time into native kernels for GPUs and ASICs. Models get lowered to a small graph of 15 primitive ops, and the compiler searches over fusion, tiling, memory and scheduling choices instead of relying on hand-written rules, which it says can find optimizations like Flash Attention on its own. The compiler is open source in Rust under Apache 2.0 or MIT, runs on CUDA and Metal with ROCm on the roadmap, and works as a torch.compile backend.
Example models: GPT-OSS 120B, Llama 3 8B
Full Luminal profileShould you choose Groq or Luminal?
Groq
Choose Groq for
- Very fast single-stream output on small open models
- Tight tail latency
- Low prices on a short model list
Luminal
Choose Luminal for
- Serving custom or fine-tuned architectures off any catalog
- High aggregate throughput on GPUs you already own
- On-prem deployments with custom kernel work and SLAs
Groq vs Luminal at a glance
| Attribute | Groq | Luminal |
|---|---|---|
| Model access | Open weights | Bring your own weights |
| Flagship models | GPT-OSS 120B, Qwen 3.6 27B | No public catalog |
| Speed | 500–1,000 tok/s | 36K tok/s on GPT-OSS 120B, 8xH100 (vendor) |
| Price | Near the floor on small models | Pay per use; rates not published |
| Customization | No fine-tuned model hosting | Compiles any PyTorch or HF model |
| Deployment | GroqCloud API | Serverless (early access), on-prem license |
| Long context | Around 131K max | Unknown |
Frequently asked questions
What is the difference between Groq and Luminal?
Groq gets speed from custom LPU chips on a small catalog. Luminal gets it from compiling models for the GPUs and ASICs you already have.
When should I choose Groq over Luminal?
Very fast single-stream output on small open models; Tight tail latency; Low prices on a short model list.
When should I choose Luminal over Groq?
Serving custom or fine-tuned architectures off any catalog; High aggregate throughput on GPUs you already own; On-prem deployments with custom kernel work and SLAs.
Is Groq or Luminal cheaper?
Groq: Near the floor on small models. Luminal: Pay per use; rates not published. The cheaper choice depends on the model and workload.
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.