Use VCF for Inference Only If vLLM Latency, GPU Scheduling, and $/M Tokens Beat the Managed API

The trade-off is simple: a private VCF endpoint replaces per-token API spend with fixed GPU, storage, network, and operations costs, so it wins only when (GPU-hours + storage + network + ops) / tokens is lower than the managed API price and p95 TTFT stays inside your SLO. If that arithmetic does not hold, the private endpoint is a compliance or control project, not a cost project.

Broadcom announced that leading AI models from providers including Google, NVIDIA, NEC, Alibaba Cloud, and Z.ai are validated to run on VMware Cloud Foundation. The announcement says VCF enables customers to bring these AI models on-premises and deliver model as a service. A Broadcom outlook is cited as saying 56% of enterprises are already running or planning to run production AI inferencing on private cloud. That market direction does not remove the engineering test: private inference must beat the API on latency, scheduling, and token economics before it earns production traffic.

Costs

Build the scorecard in US dollars per M output tokens, not per GPU. The private endpoint has five cost lines: hardware amortization, power and cooling, storage and network, platform operations, and idle capacity. The managed API has at least three: token price, rate limits or reserved capacity, and egress or data handling if applicable. The decision rule is not whether VCF can run models; it is whether your blended private $/M tokens is lower than the API price after you add the cost of meeting your SLO.

Run a short measurement window. Record input tokens, output tokens, cache hits, batch size, concurrency, and GPU utilization. Convert every line to $/M output tokens. If your private endpoint is cheaper only at peak utilization, it is not cheaper for your traffic. If it is cheaper at your actual median and p95 utilization, it is a candidate. If it is more expensive but gives data control, label it as a compliance cost and budget it as such.

Do not treat benchmark parity as a cost claim. Independent benchmark testing under MLPerf Inference v5.1 standards is claimed to show VCF delivers performance on par with bare metal. That matters because virtualization overhead can turn a cheap GPU into an expensive one when TTFT or inter-token latency drifts. The number you need is not peak tokens per second; it is sustained tokens per second at your target concurrency, with p95 TTFT and inter-token latency inside SLO.

Models

vLLM is described as the default model runtime and is said to let customers run more than 150 open source models on VCF. That is the portability baseline. Your model catalog should be judged by three properties: quantization support, context length, and serving throughput on your chosen GPU. A model that runs in a notebook is not a serving model. A model that survives your production prompt distribution, with realistic system prompts, tool calls, and retries, is a serving model.

For model portability, test the same checkpoint on VCF and on a managed API that serves the same model family. Compare quality on a fixed evaluation set, then compare latency and cost. If the private model is a different quantization, a different context window, or a different tokenizer behavior, the comparison is not parity. It is a product change. Put that in the business case.

Keep the model list narrow. A private inference platform is not a model catalog. It is a serving endpoint for the models your product actually calls. If you need many small models, batch them. If you need one large model, optimize it. If you need both, separate the GPU pools and the cost model. The scorecard should show model portability as a pass/fail list: model, quantization, context length, p95 TTFT, inter-token latency, $/M tokens, and failure mode.

Serving

Serving is where private endpoints win or lose. VCF supports mixed compute across AMD, Intel, and NVIDIA, allowing customers to choose GPU and CPU hardware for AI workloads. That flexibility is useful, but it also increases the scheduling surface. You need a GPU pool policy that says which workloads can share a device, which workloads get exclusive devices, and which workloads can be preempted. Without that policy, a low-priority batch job can push p95 TTFT above SLO.

Test bin-packing and preemption under load. Start with a steady-state load that matches production. Then add a burst. Then add a long-running request. Then add a preemptible batch job. Record p95 TTFT, inter-token latency, queue time, and GPU utilization. The private endpoint should show stable tail latency when the mix changes. If tail latency jumps when a second workload lands, your scheduler is not production-ready.

Network and storage are not background costs. Inference serving is sensitive to NIC throughput, storage IOPS, and memory bandwidth. A model that loads slowly will raise TTFT. A KV cache that spills to slow storage will raise inter-token latency. A network path that adds jitter will make autoscaling look broken. Measure the path, not just the GPU. If your p95 TTFT is dominated by model load or cache miss, fix storage and network before buying more GPUs.

Use the scorecard as a gate. Model portability: vLLM can serve the target open-source models with acceptable quality. GPU scheduling: the pool, bin-packing, and preemption policy keep tail latency inside SLO. Network and storage: p95 TTFT and inter-token latency are stable under mixed load. Benchmark parity: external benchmark results are used to check virtualization overhead, not to replace your own traffic test. Economics: private $/M tokens is lower than the managed API, or the data control and compliance value is explicitly priced. If all five pass, VCF is a private inference serving platform. If any fail, keep the managed API and revisit when the arithmetic changes.