Loading use case details
Streaming STT, LLM, and TTS on one open stack.
The optimization knobs, the codebase, the model choice. None of it locked away.
Kernels, quantization, KV cache, speculation, serving config. The agent sweeps every stage and surfaces the trade-offs.
Dockerfile, serve script, weights, and config. Yours the day you sign up. Run managed or on your own GPUs.
Paste a Hugging Face model ID. The agent checks modality and backend support, then applies the compatible tuning path for that model.
Most teams pick between speed and control. RunInfra keeps both in one workflow.
| What matters | RunInfraRecommendedFast path with model control and export. | Closed APIsFast start, locked runtime. | DIY self-hostingFull control, heavy operations. |
|---|---|---|---|
| 01Launch | Pick model, optimize, deploy Start quickly and keep the production path open. | Call provider endpoint Fast first demo, but the runtime stays rented. | Build serving stack first Infrastructure work comes before product learning. |
| 02Model control | Bring the model ID Keep model choice and serving decisions visible. | Provider catalog You use what the provider exposes. | Your model Full control if your team maintains the runtime. |
| 03Tuning | Measured latency and GPU cost Compare serving choices before deployment. | Opaque Latency and batching stay behind the API. | Manual profiling Your team owns tuning and regressions. |
| 04Export | Managed now, export when needed Use the endpoint first and take the deploy package later. | Locked endpoint You keep calling the provider. | Already owned Export exists because you built everything yourself. |
| 05Operations | Low until you choose to own it Operate managed, then export with the same measured plan. | Low, with lock-in Less infra work, less production control. | High You own infra, failures, upgrades, and serving changes. |
| 06Security | SOC 2 Type 2 Audited controls across access, logging, and incident response. | Varies by vendor Compliance depends on the third party sitting in the audio path. | You build it Your team owns the audit trail, logging, and access controls. |
Fast path with model control and export.
Launch
Pick model, optimize, deploy
Start quickly and keep the production path open.
Model control
Bring the model ID
Keep model choice and serving decisions visible.
Tuning
Measured latency and GPU cost
Compare serving choices before deployment.
Export
Managed now, export when needed
Use the endpoint first and take the deploy package later.
Operations
Low until you choose to own it
Operate managed, then export with the same measured plan.
Security
SOC 2 Type 2
Audited controls across access, logging, and incident response.
The full recipe ships with you. Codebase, kernels, engine config, weights. Run it anywhere.
Our GPUs, per-million-tokens billing from L4 to B200.
AWS, GCP, RunPod, bare metal. Same Dockerfile, your cluster.
docker compose up. Full pipeline on a single GPU.
Voice-compatible models, live from huggingface.co. Click through to inspect, or paste any compatible ID into the dashboard.
Every stage of the pipeline, retuned per model and GPU.
First Whisper word under 200ms. Mel fusion, VAD-gated decode.
FlashAttention v2 plus FlashInfer. Tuned for first-token latency.
AWQ INT4 or FP8 on the LLM. FP16 stays on Whisper for accuracy.
Paged KV with prefix cache. 60-75% VRAM savings on long sessions.
First audio frame under 200ms. Chatterbox, XTTS, Orpheus, supported open TTS.
STT, LLM, TTS interleaved on one GPU. Per-stage latency budgets.
Edit the model, engine, or GPU inline. Send to retune the stack in the dashboard.
Can't find what you're looking for? Get in touch
Does this really fit on a single L4?
Yes. Whisper Large V3 in streaming mode uses about 3 GB VRAM. Llama 3.2 3B Instruct in AWQ-int4 uses about 2.5 GB. Chatterbox vocoder is about 2 GB. The L4's 24 GB has plenty of headroom for KV cache and concurrent calls. For higher concurrency move to L40S (48 GB).
Describe the goal. RunInfra builds and optimizes the stack.
RunInfraby RightNow© 2026 RunInfra. All rights reserved.
