01
Model selection
Labs use different corpora and post-training methods, so models bend differently when tuned. Attention type, hybrid vs dense, MoE and size are all tradeoffs.
Solutions · Voice agents
Same Outcomes.
A Tenth the Size.
Voice agents live or die on speed. A model trained on your workload spends all of its capacity on your job, not on algebra your scheduler never needs.
- time to first token
- −60%
- time to first token
- time per output token
- −40%
- time per output token
- cost
- −30–50%
- cost
Why fine-tune
You are paying for capacity you never use.
TTFT and TPOT are the difference between a voice agent that feels right and an old IVR system with extra steps.
Labs build models for any use case. Your workflow has a handful. Imagine a 100M parameter model that spends 10M of its parameters on your workload. With mixture of experts the norm, that is often literally true.
A 10M parameter model can reach the same outcomes if it spends its full capacity on your workload.
The nuance
The fine-tune is easy. Every decision before it is not.
A typical fine-tune is SFT, usually as a LoRA. On paper it is simple. These are where it goes wrong.
02
LoRA vs full fine-tune
A LoRA trains 1–10% of the parameters. That is its advantage and its ceiling. Size it against task novelty and the risk of model collapse.
03
Serving
The most under-considered part. The right model for vLLM on an H100 may be wrong for SGLang on Blackwell, before you even reach architectures like Mooncake.
04
Data quality
Give me 100 fantastic examples and I’ll give you your best model, as our CTO Suriya Ganesh says. More data is often worse data.
05
SFT vs post-training
SFT is relatively simple. Post-training with RL, real environments and reward functions is harder, and it is where the large gains are.
What Parsec delivers
Not a token stamper. Not a homework shop.
Point your inference at us. We run the custom intelligence pipeline for you.
01
Evals in two weeks
Custom evals stood up from your traffic, then maintained.
02
Real post-training
Clean data and shadow environments, so we post-train like the labs do, not just SFT.
03
Served on our cluster
Aggregated deployments make custom models viable at any traffic size. Call them through the API.
The simple truth
Parsec vs DIY vs fine-tuning shops.
| Parsec | DIY | Fine-tuning shops | |
|---|---|---|---|
| Method | Post-training | Fine-tune | Fine-tune |
| Evals | Inferred from production traffic | Set up yourself | You bring them |
| Model training | Constant | Intermittent | Usually one-off |
| Serving | Optimized cluster | Stand up yourself | Your problem |
| Scale | Any traffic size | Enough for your own cluster | Enough for your own cluster |
| Cost | Your existing inference line item | ~100 hours of an experienced ML engineer | $10k minimum, often more |
Never Too Early.
Companies process 400 trillion tokens a day. We believe the next 4,000 trillion will be custom. It starts with a one-line switch.