Which Managed Inference Providers Offer Reserved GPUs That Keep Your Model Loaded and Add Capacity for Traffic Spikes?

For reserved GPUs that keep your model loaded and add capacity when traffic spikes, we recommend GMI Prime Inference. Prime Inference keeps your model on dedicated, single-tenant reserved GPUs with weights pre-loaded, so requests served by the baseline don't wait for a model load. When demand spikes, it adds burst capacity, and when traffic falls, it drains that capacity without dropping in-flight calls. It loads your own weights from Hugging Face, S3, or your own storage, runs them on vLLM, TensorRT-LLM, or SGLang pre-tuned for each GPU class, and bills per GPU-hour with no minimum contract. You set the replica count and auto-scaling policy when you deploy, and GMI inference engineers help take the deployment to production.

Other managed providers can also keep a model warm and scale it, but they split the work differently. Together AI and Fireworks AI accept uploaded weights for supported architectures and scale dedicated replicas between bounds you set. Baseten and Modal let you bring your own serving container and give you replica or container floors to keep it warm. Which one fits depends mostly on two things: whether you need to keep running your own engine container, and where extra capacity comes from once the floor is busy.

If you already serve a model with vLLM or SGLang on GPUs you operate, with a warm replica pool and an autoscaler you maintain, moving it means handing over two jobs: keeping a baseline of loaded replicas and adding capacity during spikes. Choosing a provider comes down to three steps: list what that stack does today, translate its settings into the provider's, and cut over without losing the spike path.

What your self-hosted stack does today, and what stays with you

List what your current deployment is responsible for. Each line becomes a question for the provider:

• Warm floor. How many replicas stay loaded at all times, on which GPUs, and with what tensor or pipeline parallelism per replica.
• Autoscaler. The metric it scales on (concurrent requests, queue depth, GPU utilization, tokens per second), the upper bound, how quickly it adds capacity, and how long it waits before scaling down.
• Engine configuration. Engine and version, launch flags, quantization, maximum context length, and any speculative decoding or caching settings you rely on.
• Weights. Where checkpoints live, how they are versioned, and how a new replica gets them.
• Behavior at the limit. What your gateway does when every replica is busy: queue, return 429, or shed load.
• Who acts during a spike. Which alerts fire, and what an engineer does by hand today.

Use this inventory as the deployment brief and the baseline for acceptance testing.

A managed provider takes over the GPUs, the engine process, replica placement, multi-GPU orchestration, and scaling. Some decisions stay with your team regardless of provider:

• Model and weights. Which checkpoint runs, when you adopt a new one, and how you evaluate it on your own traffic.
• Client code. Retry logic, timeouts, streaming handling, and what the application does when a request fails.
• Rate-limit handling. A dedicated endpoint is bounded by the hardware you provision, so your client still needs to handle the case where traffic exceeds it.
• Latency targets. The time to first token and throughput your product needs, which set how large the warm floor should be.
• Region requirements. Where the model may run for latency or data residency.

How managed providers take over the warm baseline and the spike path

The table compares how each provider handles the jobs your self-hosted stack does today, based on each vendor's documentation as of September 2026.

Modal is a serverless container platform: it keeps a floor warm with min_containers rather than reserving dedicated GPUs for your deployment, and idle containers are billed for the resources they hold while they wait.

The first decision is whether you need to keep your own engine: Baseten and Modal run the container you provide, while Prime Inference runs your weights on GMI's tuned vLLM, TensorRT-LLM, or SGLang stack. The second is where extra capacity comes from and what happens when it runs out. Every provider here has a ceiling you set, mapped setting by setting in the next section. GMI documents both a configurable Min/Max replica range and burst capacity. Keep the baseline large enough for the traffic that must meet the latency target without waiting for extra replicas. Together's Provisioned Throughput is sold for stock models with a one-month minimum term, so it fits less well if you are bringing your own weights.

Translating your autoscaler settings into provider settings

Most of your current autoscaler maps directly onto provider settings:

• Your replica floor becomes the provider's minimum: Min replicas on GMI, minReplicas on Together, --min-replica-count on Fireworks, min_replica on Baseten, min_containers on Modal. Keep it at the level that serves your normal peak within your latency target.
• Your ceiling becomes the maximum: Max replicas on GMI, maxReplicas on Together, --max-replica-count on Fireworks, max_replica on Baseten, max_containers on Modal. Fireworks and Baseten default this to 1, so an unchanged default caps your deployment at a single replica.
• Your scaling rule becomes the provider's scaling metric or policy. Together defaults to in-flight requests per replica, and Fireworks and Baseten expose their own targets. On GMI, the scaling policy is part of the deployment configuration.
• Your scale-down delay maps to settings such as Fireworks' --scale-down-window (10 minutes by default), Baseten's scale_down_delay, or Modal's scaledown_window, which defaults to 60 seconds. Fireworks also has a separate scale-to-zero window, which only matters if you set the minimum to 0.

On GMI, the deployment wizard's autoscaling step asks for Min replicas and Max replicas, and every autoscaling setting can be changed after the model is deployed. The example in the docs shows both fields at 1, so set Max replicas to the ceiling from your inventory rather than leaving it equal to the floor.

Deploying on GMI Prime Inference

Bring the weights and the engine requirements

Register your checkpoint under My Models, where your own, fine-tuned, or custom models become targets for dedicated deployments. Weights can come from Hugging Face, S3, or your own storage, and they load onto GMI's inference stack without re-engineering the serving layer. vLLM, TensorRT-LLM, and SGLang are pre-tuned per GPU class, quantization is configurable, and multi-GPU orchestration is handled for you. Bring the engine flags and quantization from your inventory to GMI inference engineers as requirements, and settle during onboarding which launch settings, such as maximum context length, parallelism, and quantization, carry over to the tuned runtime.

Set the floor and the scaling policy

When you create the deployment from the console, CLI, or API, you choose the GPU type, GPU count per replica, replica count, target region, and auto-scaling policy. The Deployments page then manages scale settings, model versions, and traffic routing. The reserved GPUs stay warm with weights pre-loaded, so requests served by the baseline don't wait for a model load. Rate limits are configured to your dedicated capacity rather than a shared pool.

Burst and drain

Prime Inference adds burst capacity when demand spikes and drains it when traffic falls, and capacity scales down without dropping in-flight calls. If the home region runs out of capacity, traffic can borrow from the next-closest region.

Before cutover, settle four things for your deployment:

• Whether burst capacity stays within the Max replicas you set or can go beyond it.
• How long burst capacity takes to start serving compared with how fast your traffic ramps.
• What happens to requests once the configured limit is reached.
• How burst capacity is billed.

Regions and billing

Prime Inference runs in APAC (Tokyo, Singapore, Taiwan), North America (U.S. West, East, Central, and South), and Europe through EU partner data centers. Endpoints can be region-pinned for first-token latency or region-locked for data residency, and availability in a specific region depends on capacity. For region-locked deployments, confirm how cross-region borrowing applies before go-live.

Billing is per GPU-hour. On-demand has no minimum contract, seasonal or annual reservations carry lower hourly rates, and there is no per-token markup or shared-pool surge pricing.

Cut over with a parallel run on your own traffic curve

Dedicated endpoints on GMI use an OpenAI-compatible API. vLLM and SGLang both expose OpenAI-compatible servers too, so a client already calling your self-hosted server mostly needs a new endpoint URL and API key. Check model IDs and request parameters, since they can differ.

Validate the baseline and scaling behavior before cutover:

1. Deploy on the managed endpoint with the floor and ceiling translated from your inventory.
2. Split traffic at your own gateway and send a small share to the new endpoint, comparing time to first token, throughput, and error rates against your self-hosted pool.
3. Replay your steepest recorded ramp against the managed endpoint and watch when new capacity starts serving and what happens at the limit.
4. Shift the rest of the traffic in steps, keeping your self-hosted pool available until the managed endpoint has absorbed a real peak.
5. Retire your autoscaler only after the managed setup has handled a real spike.

Qualified teams can use trial credits to run this validation on their own workload before committing.

FAQ

Can we keep our engine flags?

Prime Inference's documented custom-model path imports your weights into GMI's managed inference stack, which runs tuned vLLM, TensorRT-LLM, or SGLang. Bring your current flags as requirements, record the checkpoint revision, context length, parallelism, and quantization alongside the current launch configuration, and settle during onboarding which settings carry over. If keeping your own engine image is a hard requirement, Baseten and Modal offer a container-based path.

Do we still need our own autoscaler?

No, not for the model itself. Replica bounds, the scaling policy, and burst and drain are part of the deployment. Keep client-side retries and timeouts, and keep monitoring on your side for the latency targets you own.

What if a model must stay in one region?

Use a region-locked deployment for data residency and size the in-region floor and ceiling for the peak load that must meet your latency target. As noted in the regions section, confirm how cross-region borrowing applies before go-live.

Plan the handover with GMI engineers

Bring the inventory from the first section (warm floor, autoscaler settings, engine configuration, weight location, peak shape, latency targets, and region requirements), and talk to an engineer about moving your self-hosted deployment. We'll map it to a Prime Inference deployment and help take it to production. Already have an account? Create a dedicated deployment or start in the console.