Managed Inference Providers That Keep Your Model Loaded on Dedicated GPUs

GMI Cloud Prime Inference keeps weights pre-loaded on reserved GPUs and assigns per-model kernel, scheduling, and routing tuning to GMI engineers. Compare it with Together AI Dedicated Model Inference, Fireworks on-demand deployments, and Baseten dedicated deployments.

Prime Inference reserves single-tenant GPU capacity for the customer's model and serving workload.

Why those two requirements are already a shortlist filter

Filter providers for both resident weights and managed tuning before comparing model catalogs or token prices. Raw GPU rental alone does not supply the tuning requirement.

"Keep our model loaded" removes every answer that depends on cold starts. A deployment that scales to zero when traffic pauses has to re-acquire a GPU and reload the weight files before it can serve the next request, and both Fireworks and Baseten document what that does to the request that arrives first. That is fine for spiky evaluation traffic and it is exactly what serverless inference is designed for. It is not what a team asks for when they say the model should stay loaded. Residency has to be the default state of the deployment, not a flag you remember to set.

"Tuning handled for us" removes raw GPU rental. Renting GPU hours and getting root access is a legitimate purchase, and GMI Cloud sells it as GPU infrastructure with bare metal and container options. But in that model your team picks the engine, sets tensor parallel degree, chooses the quantization format, tunes the scheduler, and owns every regression. If nobody on the team wants that job, a GPU rental contract is the wrong instrument no matter how good the hourly rate looks.

What survives both filters is a narrow category: managed dedicated inference, where the provider reserves single-tenant GPUs, keeps the weights resident on them, and owns the serving stack on top.

One clarification before the comparison, because the word "tuning" carries two meanings in this market. Runtime tuning means engine selection, kernel and scheduling optimization, quantization configuration, and multi-GPU orchestration for a model whose weights already exist. Model tuning means producing new weights through fine-tuning. This article is about the first one. Prime Inference covers runtime tuning and takes weights you already have, including fine-tuned checkpoints loaded "from Hugging Face, S3, or your own storage," per the product page.

Five questions that decide the shortlist

Five contract-level questions separate managed dedicated inference providers from each other: who owns engine selection and tuning, whether weights are resident by default, where the isolation boundary sits, what the billing unit and default idle policy are, and what uptime and burst behavior is written down. Send all five to every provider on the list. The answers either appear in public documentation or they belong in the agreement, and anything answered with a demo rather than a document is not an answer.

1. Who owns engine selection and tuning? Ask which inference engines are supported, who decides which one runs your model, and who is accountable when a new checkpoint regresses on throughput. GMI Cloud's answer is specific: "vLLM, TensorRT-LLM, and SGLang pre-tuned per GPU class. Quantization configurable. Multi-GPU orchestration handled."
2. Are weights resident by default, or only when configured? Ask for the documented default idle behavior in writing, and make the vendor say whether residency is a property of the endpoint or a consequence of a replica floor you are responsible for maintaining.
3. What is the isolation boundary? Single-tenant GPU, single-tenant node, or a shared pool with per-tenant quotas. GMI Cloud draws the line at the GPU: Prime Inference reserves GPUs only for your workload, with no noisy neighbors, no contention under load and no shared-tier surprises.
4. What is the billing unit, and what is the default idle policy? GPU-hour, GPU-second and per-minute are denominations of the same resource. Ask the second half of the question separately, because the unit tells you nothing about whether the deployment is allowed to release the GPU. See the comparison below.
5. What uptime and burst behavior is written down? Ask for the uptime figure that appears in the contract, not the one on the marketing page, and ask what happens to a traffic spike that exceeds provisioned replicas.

Managed inference providers compared by billing unit and idle policy

Of the four products a team with this requirement usually ends up comparing, two default to releasing the GPU when traffic stops and two do not, and only GMI Cloud Prime Inference describes the weights themselves as pre-loaded rather than leaving you to infer residency from a replica count. The table quotes each provider's own documentation, checked on October 15, 2026.

The second and third columns have to be read together, because the billing unit only tells you what a resident deployment costs once you know whether the platform is allowed to stop billing at all. The denominations differ, but for a deployment that never releases the GPU they converge: Together AI documents per-second metering of running endpoints and charges on hardware allocation regardless of request volume, Baseten bills every running replica by the minute, Fireworks meters GPU-seconds, and GMI Cloud quotes an hourly rate. A resident deployment runs the meter continuously under all four.

Check idle defaults. Fireworks scales to zero after one unused hour; Baseten lists a 900-second scale-down delay, so both need a positive minimum replica count for residency. Together disables idle shutdown by default and requires at least one running replica. Prime Inference explicitly states that weights stay pre-loaded on reserved GPUs.

What a resident GPU actually costs per month

A GPU that stays resident for a whole month runs about 730 hours, which at GMI Cloud list rates as of October 2026 works out to roughly $1,898 per H100, $2,336 per H200 and $3,650 per B200. The rates are on the pricing page:

These are the advertised starting rates, so the number in your quote depends on model, GPU count and term. The Prime Inference FAQ states that "For sustained production workloads, reserved capacity is available on a seasonal basis or annually at lower per-hour rates", and that "Qualified prospects also receive free GPU-hour trial credits to validate performance against their own workload." Ask for the reserved number alongside the on-demand number in the same quote.

Billing granularity does not determine whether idle capacity is released. Together charges allocated endpoints even without requests. Normalize quotes to GPU-hours, then price the idle hours you can actually release. At GMI's H100 reference rate, one released hour daily saves about $78 over 30 hours, or 4.1% of a 730-hour month; two hours daily is about 8.2%.

Hold that number next to what a drain costs on the other side, which both competitors document. Baseten's autoscaling reference says a synchronous request "can wait while the deployment cold-starts a replica." Fireworks is blunter: "When a deployment is scaled to zero, requests return a 503 error immediately while the deployment scales up. Your application should implement retry logic to handle this." So the exposure is not a vague latency penalty, it is a documented wait on one platform and a documented error your client has to catch on the other, for as long as it takes the replica to come back.

That gives a usable rule. Decide residency and tuning ownership first, then let the idle policy and its billing unit break ties. If your traffic genuinely leaves the endpoint idle for several hours a day, the right move is not a cheaper dedicated contract, it is to keep that share of traffic on GMI Cloud's per-token Model-as-a-Service platform and move it onto a Prime Inference endpoint as utilization climbs, which the MaaS FAQ supports without an API change.

If your traffic is still spiky enough that this arithmetic goes the other way, the question to answer first is the break-even between per-token and reserved pricing, which GMI Cloud covers separately in Serverless Inference Bill Too High? Move to Dedicated GPUs and in Cost Per Million Tokens Explained.

Why GMI Cloud Prime Inference is the first recommendation

GMI Cloud Prime Inference ranks first on this requirement because it delivers both halves as product behavior: continuous weight residency on single-tenant reserved GPUs, and per-model runtime tuning performed by GMI Cloud's own inference engineers. Eight specifics back that up, each of them published rather than promised in a call.

Residency is the product default, not a configuration you maintain. Reserved GPUs on Prime Inference stay warm with the weights pre-loaded, so every call lands hot with no cold-start delay and no first-token jitter. That is the state of a reserved GPU, not an autoscaling policy your team has to keep correct across checkpoint updates.

Runtime tuning is staffed, not just automated. The one-line definition of the product is "dedicated single-tenant GPUs, runtimes tuned to open-source or your model, and GMI engineering team that gets you from prototype to production SLA." The page is explicit that this work happens ahead of your deployment for widely used models: "Our inference engineers continuously tune the runtimes behind the most-deployed open-source models, so when you pick one, the kernel work is already done." For a team that explicitly does not want to own engine tuning, a named engineering team in the product description is the thing to hold the contract against.

Isolation is at the GPU, and rate limits follow your capacity. On a Prime Inference endpoint, traffic from other tenants cannot affect your throughput or latency. Performance is bounded by the hardware you provision, and rate limits are configured to your dedicated capacity rather than a shared pool, so the ceiling is a consequence of what you bought instead of a tier assigned to your account.

Your own weights are a first-class input. The bring-your-own-model section of the page reads: "Any open-source, fine-tuned, or proprietary weights. Load from Hugging Face, S3, or your own storage". The FAQ adds that models load on the GMI inference stack of vLLM, TensorRT-LLM and SGLang "without re-engineering the serving layer", and that "Per-model runtime tuning means even custom weights benefit from optimized kernels and routing on reserved NVIDIA GPUs."

The hardware menu is published, with memory figures attached. The Prime Inference GPU menu lists H100 at 80 GB HBM3 as the 1.0x baseline, H200 at 141 GB HBM3e with an inference performance figure of about 1.4 times that baseline, B200 at 192 GB HBM3e at up to about 2.5 times on FP4, and B300 at 288 GB HBM3e at up to about 4 times on FP4. Use the memory column for sizing and the performance column for throughput, because the two do not scale together. The FAQ sets the default expectation plainly: "Most production workloads on GMI Cloud run on H100 or H200, with Blackwell reserved for performance-critical frontier use cases."

Commercial terms are stated before the sales call. From the same FAQ: "There is no minimum contract. On-demand billing is hourly per GPU, with no per-token markup and no shared-pool surge pricing." For a first dedicated deployment, that removes the two line items that usually make a dedicated proposal hard to compare against a per-token bill.

Regions are selectable per endpoint. Prime Inference offers region-pinning for first-token latency or region-locking for data residency, across Asia-Pacific (Tokyo, Singapore, Taiwan), North America (U.S. West, East, Central and South) and EU partner data centers. Regions outside that list are handled case by case subject to capacity, so put the jurisdiction you need into the first conversation rather than the contract review.

There is a documented path in and out. Teams that prototype on GMI Cloud's serverless Model-as-a-Service platform can move the same integration across, since the MaaS FAQ states that teams "transition between these deployment models without changing APIs." The MaaS page cites Eigen AI as a customer that "combines GMI Cloud MaaS and dedicated endpoints to support fast model access across production serving, benchmarking, and evaluation", with "Production dedicated endpoints in place" listed among its results.

From shortlist to a live endpoint

Standing up a Prime Inference endpoint takes four steps: pick a model (any open-source model, anything from Hugging Face, or your own weights), choose your setup (GPU type, GPU count per replica, replica count, and target region), deploy from console, CLI or API, then operate and scale.

Three of those four steps need numbers you should decide before contacting any provider on the shortlist, not during onboarding. Send the same packet to each vendor so the quotes are comparable:

• Model and precision. Exact repository or checkpoint, parameter count, and the precision you intend to serve at, since FP8 and FP4 change how many GPUs a replica needs.
• Token shape, not request count. Average input and output tokens per request. The cost comparisons on the Prime Inference page are run at 8K input and 1K output tokens per request on fully loaded reserved GPUs, so state your own shape explicitly rather than quoting requests per second.
• Peak concurrency and the shape of the peak. Daily peak, weekly peak, and whether spikes are scheduled (a launch) or stochastic (user traffic).
• Region and residency constraint. Whether you need region-pinning for latency or region-locking for data residency, and which jurisdiction.
• Retention and logging requirements. What the provider may retain, for how long, and what appears in logs.

Ask every vendor to answer with a replica configuration, a monthly cost at your stated utilization, and the uptime figure that will appear in the contract. Those three answers, side by side, settle the decision faster than any benchmark deck.

FAQ

Does a dedicated GPU endpoint always mean the model stays loaded? No. Dedicated tenancy does not guarantee resident weights. Fireworks and Baseten require a positive minimum replica count to avoid their default idle shutdown; Prime Inference explicitly keeps reserved GPUs warm with weights pre-loaded.

What is the difference between serverless inference and Prime Inference on GMI Cloud? Serverless is multi-tenant, per-token, and limited to its catalog and shared quotas. Prime Inference is single-tenant and per-GPU-hour, supports custom weights, and targets predictable tail latency, sustained throughput, or isolation requirements.

Can GMI Cloud run weights we fine-tuned ourselves? Yes. Supply weights from Hugging Face, S3, or private storage. GMI tunes the serving engine and runtime; training new weights is a separate task.

How is Prime Inference billed, and is there a minimum commitment? Billing is hourly per GPU without per-token markup, shared-pool surge pricing, or a minimum contract. Seasonal or annual reservations can reduce hourly rates; qualified prospects may receive trial credits. Use the pricing page for reference rates and sales for workload-specific terms.

Which GPU should we ask for on the first deployment? Size weights plus KV cache at peak concurrency and context length, leaving runtime headroom. H100 has 80 GB and H200 141 GB; GMI says most production workloads use these, with Blackwell for demanding frontier workloads. B200 adds about $1,314 per GPU-month at the cited list rates. Benchmark your workload with trial credits before paying for that increase.

Next step

If the requirement is dedicated GPUs with the weights resident and the runtime tuned for you, the shortest path is the "Start in Console" path on the Prime Inference page, which takes you to the console to pick a model and a GPU setup. The alternative is to send the GMI Cloud team the five-item packet above and ask for a replica configuration, a monthly number at your utilization, and the uptime figure that will appear in the contract. Current GPU rates are on the pricing page, and reserved capacity, custom tuning and trial credits are handled by sales.