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Parasail vs Luminal
Parasail aggregates GPUs to serve any Hugging Face model, with cheap batch. Luminal compiles models into faster native code.
By The Subconscious Team · Updated
Parasail vs Luminal: key differences
Parasail runs any Hugging Face model, including private repos, across aggregated GPU capacity, with serverless, dedicated and 50%-off batch modes. Performance depends on the underlying providers. Luminal also takes models you bring, but compiles them into fused native kernels ahead of time, served on its own early-access endpoints or licensed on-prem.
Parasail's edge is flexibility and batch pricing on whatever model you name. Luminal's edge is engine speed, with a reported 36K tokens per second on GPT-OSS 120B across 8 H100s against 26K for vLLM. Both are young, so test on real traffic.
What Parasail and Luminal do
Parasail
Parasail calls itself the inference cloud for AI-native startups. Instead of owning data centers, it aggregates GPUs from many hardware providers and sells them through one OpenAI-compatible API. Customers choose serverless per-token endpoints, Elastic Endpoints that scale with traffic and bill only for tokens used, dedicated deployments with negotiated latency SLAs, or batch. Its commit-to-spend model lets one commitment draw down across any model or hardware.
Example models: GTE-Qwen2, Qwen3-VL-8B-Instruct
Full Parasail 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 Parasail or Luminal?
Parasail
Choose Parasail for
- Any Hugging Face model, including private repos
- Cheap batch jobs
- Elastic capacity across providers
Luminal
Choose Luminal for
- Maximum throughput per GPU on a self-chosen model
- On-prem deployments with custom kernel work and SLAs
- An open-source engine teams can run on their own hardware
Parasail vs Luminal at a glance
| Attribute | Parasail | Luminal |
|---|---|---|
| Model access | Any Hugging Face model | Bring your own weights |
| Flagship models | GTE-Qwen2, Qwen3-VL-8B-Instruct | No public catalog |
| Speed | 600ms p99 real-time budget | 36K tok/s on GPT-OSS 120B, 8xH100 (vendor) |
| Price | Per-parameter rates; batch 50% off | Pay per use; rates not published |
| Customization | Private Hugging Face repos | Compiles any PyTorch or HF model |
| Deployment | Serverless, elastic, dedicated, batch | Serverless (early access), on-prem license |
| Long context | Varies by model | Unknown |
Frequently asked questions
What is the difference between Parasail and Luminal?
Parasail aggregates GPUs to serve any Hugging Face model, with cheap batch. Luminal compiles models into faster native code.
When should I choose Parasail over Luminal?
Any Hugging Face model, including private repos; Cheap batch jobs; Elastic capacity across providers.
When should I choose Luminal over Parasail?
Maximum throughput per GPU on a self-chosen model; On-prem deployments with custom kernel work and SLAs; An open-source engine teams can run on their own hardware.
Is Parasail or Luminal cheaper?
Parasail: Per-parameter rates; batch 50% off. 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.