Which Providers Can Start You on APIs, Then Move You to Dedicated GPUs With Help Tuning Inference?

When an open-source model goes from prototype to steady traffic, billing moves from per token to per GPU-hour, and GMI Cloud covers both stages: the serverless MaaS API with no commitment while you prototype, then Prime Inference with single-tenant GPUs, a runtime tuned to that model, and engineers who work with you from prototype to production SLA.

With both stages at one provider, the model choice and the test set carry over, and replaying that test set on the dedicated endpoint confirms quality before traffic moves. A move to a different vendor's dedicated tier can mean adapting your evaluation harness to new endpoints and parameters, and re-testing quality against that vendor's serving stack.

Four properties of an API-to-dedicated path

Two products sold side by side are not an API-to-dedicated path. The path has these properties:

• The same model on both sides. You should be able to prototype on a hosted open-source model and then deploy that same model on reserved hardware, so the evaluation set you built on the API can be replayed against it.
• Dedicated means single-tenant. The GPUs are reserved for your workload only, with no shared pool setting your latency.
• Tuning is part of the service. Engine selection, quantization, batching and routing should be done by the provider's inference engineers, not left as a blank vLLM install.
• A way to prove it first. No minimum contract, or trial capacity, so you can test on your own traffic before committing.

GMI's two products map onto these. MaaS is multi-tenant, billed per token, and aimed at prototyping, low-volume use, and elastic traffic. Prime Inference is single-tenant, billed per GPU-hour, and aimed at sustained production traffic. Both are called through an API, so the base URL, model ID and API key belong in configuration rather than in code.

When to stay on the API, and when to move

The API is the right place to be until one of these pressures shows up and justifies a dedicated endpoint:

Signal	What it looks like	Why dedicated helps
Steady utilization	Traffic keeps a GPU busy for most of the day	Per-token pricing stays flat as you scale; GPU-hour pricing gets cheaper per token as utilization rises. For the models and configurations GMI benchmarks on Prime Inference, the break-even sits at roughly 35% to 45% sustained utilization, and it depends on your traffic shape
Tail latency	Your product needs a tight P95 or P99 target	Reserved GPUs serve only your traffic, so tail latency is set by your own load and the hardware you chose, not by other tenants
Rate limits	Launches or batch jobs need more throughput than your usage tier	A dedicated endpoint has no shared rate limit; the cap is the hardware you provision
Warm capacity	Every call needs to land on a loaded model	Reserved GPUs stay warm with weights loaded
Isolation or residency	A customer or auditor needs a private runtime or a fixed region	Single-tenant isolation, region-pinning or region-locking, and audit logs are part of the Prime offering

Staying put is also a valid answer. A prototype, an internal tool, or a product with spiky, mostly idle traffic fits the serverless API, which bills per token with no commitment. A dedicated GPU bills while it is running, so it pays off when the work you push through it is steady.

You do not need to know the exact crossover before you move. Log your own token volume, request lengths and peak concurrency over a week of real traffic. Then measure what throughput a candidate dedicated configuration delivers for your model, precision and latency target, and compare the two on the same time window. GMI's team will model the break-even against your token volume if you share it.

The migration, step by step

1. Freeze an evaluation set. Collect real prompts, expected outputs or scoring rules, and the latency you need at P50 and P95. Keep it fixed so every result is comparable.
2. Record the API baseline. Run the set against the serverless endpoint and save quality scores, time to first token, tokens per second per user, and error rates. This is the bar the dedicated endpoint has to meet or beat.
3. Isolate the endpoint in your code. Put the base URL, model ID and API key behind configuration. On GMI's LLM API, the chat completions endpoint is https://api.gmi-serving.com/v1/chat/completions; a dedicated endpoint has its own URL, shown in the console once the deployment is running.
4. Choose the setup. On the Prime Inference path you pick a model, then GPU type, GPUs per replica, replica count and region. The Dedicated Endpoint guide walks through the console flow: open Deployments, click Create Deployment, choose the model, region, GPU type and scaling, and deploy.
5. Deploy and wait for Running. Deployments pass through Queued, Deploying and Running. You are billed only while a deployment is in Running status, and you can stop one and restart it later. A Prime Inference endpoint goes live in minutes rather than days, and you can launch it from the console, CLI or API.
6. Replay and tune. Send the frozen set to the new endpoint at your expected concurrency, using the same model version. Compare quality first, then latency and the throughput the configuration sustains against the baseline. Then raise concurrency until quality or latency reaches your target; the highest throughput that still meets both is the capacity figure for your utilization math, measured with the same request mix and token lengths as the baseline. If the numbers fall short, work with GMI's inference engineers on engine, quantization and replica settings and replay again. Finish this before any production traffic moves.
7. Shift traffic gradually. Route a small share to the dedicated endpoint, watch error rates and tail latency, then raise the share. The same engineers can retune the setup if production traffic looks different from your evaluation set. Keep the serverless endpoint configured as a fallback and as overflow for bursts beyond your reserved capacity, but only for traffic that is allowed to run on shared infrastructure under your data policy. Traffic that moved because of isolation or residency requirements should stay on the dedicated endpoint: tag those requests in your gateway so they queue or fail instead of silently falling back to shared capacity.

Tuning scope for the model you prototyped

On Prime Inference, GMI's side of the tuning work covers:

• Per-model runtime tuning. Kernel, scheduling and routing optimization for the model you pick, instead of a generic stack.
• Engine selection. vLLM, TensorRT-LLM and SGLang, pre-tuned per GPU class.
• Precision. Quantization is configurable, which lets you try lower-precision serving against your quality bar.
• Multi-GPU orchestration. Handled by the platform when a model needs more than one GPU.
• Always-loaded weights. Reserved GPUs hold the model in memory between requests.
• Capacity modes. Guaranteed performance comes from reserved capacity, spikes go to burst capacity, and scale-down is graceful when load falls.

The runtimes behind the most-deployed open-source models are already tuned by GMI's inference engineers, so much of the kernel work is done before your first deployment. One-click deployment covers DeepSeek, Kimi, GLM, Llama and NVIDIA models, including DeepSeek V4, Kimi K3 and GLM 5.2.

Your side is the evaluation set, the quality threshold that decides whether a quantized build is acceptable, the latency budget, and the traffic forecast.

This path applies to moving the same open model. Prime Inference also supports custom and fine-tuned weights.

Choosing hardware for the dedicated stage

Prime Inference offers four GPU classes:

GPU	Memory	Where it fits
H100	80 GB HBM3	General LLM and multimodal inference; where most production workloads start
H200	141 GB HBM3e	Long context, large KV cache, big batch sizes
B200	Blackwell, pre-order	Frontier models and FP4 inference
B300	288 GB HBM3e, pre-order	Very large models and ultra-long context, typically across several GPUs

H200 is available now on GMI Cloud, so it is the practical first target for a dedicated deployment, provided the model's weights, context length and concurrency fit the number of H200 GPUs you provision. B200 and B300 are in pre-order, so plan them as a second step: build the migration around H200, then move to Blackwell once capacity is scheduled, using the same evaluation set to check the gain.

Cost and commitment terms

There is no minimum contract on Prime Inference. You pay by the GPU-hour on demand, with no per-token markup and no surge pricing from a shared pool. For sustained production workloads, reserved capacity is available on a seasonal or annual basis at lower hourly rates, and qualified prospects get free GPU-hour trial credits to test on their own workload. GMI sales can quote current GPU rates for your model and traffic profile.

Prime Inference lists a 99.9% uptime SLA figure; the committed SLA depends on GPU series, deployment configuration and contract.

Deployments can be placed in Tokyo, Singapore or Taiwan for Asia-Pacific; U.S. West, East, Central or South for North America; and EU partner data centers for Europe. Pin an endpoint to one region to cut first-token latency, or lock it there for data residency.

Week one on MaaS, then a Prime Inference endpoint

Begin on the serverless API with the open model you expect to ship, picked from the MaaS page. Build the evaluation set while you prototype, and log tokens, latency and utilization from day one. Once a week of real traffic shows sustained load, or a latency, isolation or rate-limit requirement calls for reserved capacity, deploy the same model on a Prime Inference endpoint sized for it, starting on H200, and run the migration steps above.

An endpoint can be created directly from the console; reserved capacity, tuning and trial credits are arranged through GMI sales.