Dedicated inference from activation to a working run: the pitfalls I hit

I spent two days trying out Together AI's Dedicated Model Inference. The name sounds intimidating, but it's really just one thing: don't squeeze into the same batch of GPUs as everyone else, reserve your own and run the model.

First, two terms. Normally when people call models, it's mostly token-priced shared APIs — you send a request, the provider allocates you a slice of compute from a public pool. It's like a public cafeteria, charged by portion, cheap, but you queue at mealtime. Dedicated inference is reserving your own stove — the GPU only runs your work, and the fee is based on reserved machines and duration. In the material, DigitalOcean describes this as "reserved capacity eliminating the fluctuation of shared resources," and CoreWeave breaks it into two steps, building a gateway and building a deployment. The wording is basically consistent — three ways of saying the same thing.

First, whether you should reserve this stove. The criterion is really just one: whether your call volume is steady every day. Small volume, big fluctuation — just use the shared API. Large volume, running all day, and latency drifting breaks your business — that's when dedicated comes in. The piece I wrote earlier on dismantling AI server recycling follows the same path: look at the cost structure first, then the conclusion, don't get led around by marketing wording. This product offers a 99% availability SLA and reserved throughput, which in plain terms means the machines are held for you and there's a fallback commitment if something goes wrong. The cost is you pay even when idle.

From zero to getting the first request through, here's my complete path — menu names may differ by a word or two across accounts, but they're all under the deployment category.

1. After registering, enter the console, find Dedicated or Models on the left, click in and you get the model list.

2. Pick a model. The list has open-source models like Llama and Qwen. Look at two columns: context length and quantization version. Context length is how much text you can stuff in at once; quantization version can be understood as a compressed model — runs faster, uses less VRAM, answer quality drops a bit.

3. Pick hardware and replica count. Replica count is how many machines run at once; 1 is enough to get through, 2 or more before you talk throughput. Fill in 1 the first time.

4. Give the deployment a name, submit. Status shows Pending first; mine took about ten-plus minutes to turn Running. This is the step where you're most tempted to refresh the page — don't.

5. Once it's Running, the page gives a base URL and an API key. It's compatible with the existing API format, so in code you basically change two lines — swap base_url for the new one, and the model name for the one you deployed.

6. Send one minimal request first, don't jump straight to load testing.

7. Once it works, run a round with real data.

The pitfalls are all concentrated in steps 5 and 6. My first request errored because I filled in the model name shown on the list page, when you actually need the ID from the deployment detail page. The second pitfall is cold start — the first few requests right after Running are noticeably slow; don't take that as a performance conclusion. The third pitfall is concurrency — following the shared API habit, I threw dozens of concurrent requests at it, but on the dedicated side the replica count is fixed, so the excess just queues. That time I under-configured my own replicas.

Now pros and cons. Latency is steady — the shared pool drifts at evening peak, while dedicated in my testing is basically a flat line; cost is predictable, calculated by reservation not by token, easy to write into a budget; migration cost is low, compatible with the existing API, small changes. Conversely, you pay when idle, and at small volume it's much more expensive than per-token; elasticity is poor, sudden traffic needs waiting to add replicas, unlike the shared pool which auto-scales; ops responsibility increases — picking models, tuning replicas, watching utilization — work the provider used to do, now yours.

My judgment is it suits scenarios with steady daily call volume and latency sensitivity, not businesses that are quiet by day and explode at night.

Having learned this, what to try next is: export your current call logs, plot a curve by day, and see how many times the peaks and troughs differ. Small difference, switch to dedicated; big difference, keep shared for now, or run both. Reserving the stove ultimately comes down to doing the math.