01
Model selection
Attention type, hybrid vs dense, MoE, parameter count. The wrong start caps results before you train a step.
Compare · Your own GPUs
Own the Models.
We Own the Pager.
Owning your intelligence is the right instinct. The question is whether you build and run that pipeline on your own GPUs, or point your inference at Parsec and let us run it.
The basics
Four boxes on a whiteboard. Two projects in practice.
Pick an open-weight model, fine-tune it on your traces, rent GPUs, serve it. The model you pick decides how it serves. The hardware decides which models are viable. The data decides whether any of it was worth doing.
None of it is one-time work. The model that was right in March is stale by June.
Part one · The model work
The fine-tune is the easy part.
02
Data quality
100 fantastic examples beat a pile of mediocre ones. Curation trips up experienced teams most.
03
LoRA vs full
A LoRA trains 1–10% of the parameters: its advantage and its ceiling. There is no default answer.
04
SFT vs post-training
RL with real reward functions and shadow environments is where the large gains live, and where most teams lack infrastructure.
05
Evals
You cannot tune what you cannot measure. Keeping evals current with your traffic is its own project, and usually the one skipped.
Part two · The serving work
Then you have to run it.
01
Hardware fit
The right model for vLLM on an H100 may be wrong for SGLang on Blackwell. Decide before you fine-tune.
02
Procurement
Quotes, waitlists, and capacity that isn’t there the week you need it.
03
The stack
Drivers, CUDA, quantization, batching, cache management, then architectures like Mooncake for latency.
04
Operations
Monitoring, autoscaling, failover, on-call. When a node dies at 2am, your engineer gets paged.
05
Cost shape
Reserved GPUs bill busy or idle. One model rarely has the traffic to keep its cluster busy.
06
Headcount
100+ hours of an experienced ML engineer to first deployment, and part of that person forever after.
What Parsec delivers
Custom intelligence as a service.
01
One line
Point your inference traffic at Parsec.
02
Weeks 1–2
Evals from your production traffic. Data cleaned. Shadow environments test new models without touching users.
03
Weeks 2–4
Your first post-trained model ships. Real RL post-training, not a one-off SFT.
04
Ongoing
Training never stops. Models shift as your traffic does. Typical gains within three months.
05
A team of models
The universal K/V cache routes every turn, so a small model handles routine work and a stronger one steps in when needed.
06
Shared serving
Your models run alongside other teams’ on an optimized cluster. Nothing to procure, tune, or babysit.
The simple truth
Parsec vs your own GPUs.
| Parsec | Your own GPUs | |
|---|---|---|
| Method | Post-training with RL | SFT, usually LoRA |
| Evals | Inferred from production traffic, maintained | Set up and maintained yourself |
| Data cleaning | Done for you | Your problem |
| Model training | Constant | Intermittent, usually one-off |
| Routing | Per turn, team of models | One model per task |
| Model ownership | You | You |
| Hardware | None to procure | Source, negotiate, wait |
| Serving | Optimized shared cluster | Stand up and tune yourself |
| Scale to make it pay | Any traffic size | Enough to fill your own cluster |
| On-call | Parsec | Your engineers |
| Time to first custom model | About 4 weeks | 100+ ML engineer hours, plus procurement |
| Cost | Your existing inference line item | GPU reservation plus headcount |
When DIY is still right
Parsec is not for everyone.
Run your own if any of these apply. Otherwise you are about to spend a quarter rebuilding something that exists.
01
Air-gapped
Residency rules prohibit any external endpoint.
02
Custom stack
Custom kernels, non-standard architectures, or a modified serving stack.
03
Steady scale
Enough traffic to keep a dedicated cluster busy, and an ML team with cycles to spare.
04
Bit-exact
Reproducibility pinned to one hardware and software combination.
Your Engineers Never Touch a GPU.
60% savings on real workloads, 5 to 15% better quality, and for voice agents 60% lower TTFT and 40% lower TPOT.