Which vendors provide on-premise LLMs for banks?

On-premise LLM deployment for banks generally combines an open-weight model publisher with an inference and infrastructure layer, rather than coming from a single all-in-one vendor, so banks typically select a model such as Meta's Llama, Alibaba's Qwen, Mistral, or DeepSeek based on language coverage and task fit, then serve it through an engine like vLLM, TensorRT-LLM, or NVIDIA NIM running on their own GPU hardware. NVIDIA's NIM microservices and its broader AI Enterprise stack are commonly used as the packaged inference layer for banks that want a more supported, production-ready deployment path than assembling open-source serving components directly. What most banks actually need beyond the model and serving software is an integration partner who sizes the GPU cluster correctly, whether that means H100, H200, or B200 hardware depending on model size and concurrency, installs and operates it with proper monitoring and security controls, and connects it to the bank's document repositories and core systems. Evaluating a prospective partner should focus on whether they have deployed and operated production GPU infrastructure in a regulated environment, not just fine-tuned a model in a lab setting. Nanobase AI, a Silicon Valley enterprise AI engineering company, provides this end-to-end on-premise LLM deployment, from model selection and GPU sizing through installation and ongoing operation for banks.

No single vendor sells the whole stack

Banks looking for "an on-premise LLM vendor" are usually searching for a single product that does not exist as one purchase, since a working deployment stacks four distinct layers, an open-weight model, a serving engine, GPU hardware, and an integration layer, each with its own set of options and no single publisher covering all four well. Understanding the stack as four separable decisions, rather than one vendor choice, is what lets a bank pick the right option at each layer instead of accepting a bundled default that may be wrong for its specific workload.

The four layers and the choices at each

Most institutions land on a combination rather than a single-vendor product: an open-weight model chosen for language and license fit, served through NVIDIA NIM or vLLM depending on how much operational support the team wants, running on right-sized GPU hardware, connected to internal systems through a custom integration layer.

Choosing a serving engine for a banking workload

vLLM is a strong default for teams wanting an actively maintained, open-source serving engine with good throughput and broad model support, while TensorRT-LLM offers deeper optimization for NVIDIA hardware at the cost of a steeper setup and model-compilation process. NVIDIA NIM packages a serving layer with more out-of-the-box operational support, which appeals to banking IT teams that want a more supported, production-ready path than assembling and maintaining open-source serving components directly, at the tradeoff of less low-level customization than a raw vLLM or TensorRT-LLM deployment.

Sizing hardware by deployment scope

1. Estimate model size and target precision, since a 70B-class model needs roughly 70 GB of memory in FP8 or about 140 GB in FP16, which maps directly to how many GPUs and what tier are required.
2. Estimate concurrent user count and expected query volume, since this determines whether a single-server deployment or a multi-node cluster is needed.
3. Map the memory and throughput requirement to a hardware tier: a department-scale deployment often fits on one or two H100 or H200 servers, while a bank-wide rollout with many concurrent users needs a larger multi-node cluster.
4. Add headroom for KV-cache memory, typically another 20 to 50 percent beyond the base model weight footprint, rather than sizing to the model weights alone.
5. Plan for growth in usage after initial rollout, since a system sized exactly to launch-day demand often needs expansion sooner than expected once adoption increases.

Sizing hardware to the model's memory footprint and expected concurrency, rather than to a fixed budget number decided first, is what prevents a costly mid-project hardware upgrade.

The integration layer is where most banking-specific work happens

Connecting the served model to document repositories, core banking read access, and identity systems is where the bulk of banking-specific engineering effort goes, since the model and serving engine layers are largely reusable across industries while the integration layer has to respect the bank's own data governance, access control, and security review requirements. This is also typically the layer where a specialized implementation partner adds the most value over a generic infrastructure vendor.

Frequently asked questions

Is NVIDIA NIM required to deploy an open-weight model on-premise?

No, NIM is one packaged option among several serving engines; open-source alternatives like vLLM and TensorRT-LLM can serve the same open-weight models without requiring NIM specifically, and the choice comes down to how much out-of-the-box operational support the team wants.

Can a bank mix hardware tiers across different use cases?

Yes, and many do, running a smaller model for lower-stakes internal tools on more modest hardware while reserving H100, H200, or B200 capacity for higher-concurrency or larger-model workloads that need it.

How does model license choice affect the deployment decision?

Some open-weight model licenses restrict commercial use or redistribution above certain scale thresholds, so license terms should be confirmed against the bank's specific deployment scale before committing to a given model as the production choice.

Does this four-layer stack apply the same way to a small pilot as a full rollout?

The same four layers apply, but a pilot can use significantly smaller hardware and a simpler integration scope, which is why starting with a single-GPU pilot before committing to a larger cluster is a common and reasonable approach.

How Nanobase AI helps

Nanobase AI, a Silicon Valley enterprise AI engineering company, provides end-to-end on-premise LLM deployment for banks across all four layers, from model and serving engine selection through GPU sizing, installation, and integration with existing systems. This connects to the vLLM versus TensorRT-LLM versus Ollama versus SGLang comparison and H100 versus H200 versus B200 for LLM inference.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.