How to choose the right open-source model and hardware

Deploying a private LLM starts with two decisions that are deeply intertwined: which model to run, and what hardware to run it on. Get the pairing wrong and you either overspend on capability you do not use or undersupply the compute that your use case actually needs. The good news is that the open-source ecosystem has matured to the point where there is a well-tested model for almost every budget and task — if you know how to match them.

Start from the use case, not the benchmark

The most common mistake in model selection is leading with benchmark scores rather than task requirements. A model that achieves state-of-the-art results on a coding benchmark may be overkill for summarising support tickets, and may introduce latency that makes it unsuitable for real-time use. Before choosing a model size, define your use case precisely: What is the average input length in tokens? Does the task require multi-step reasoning, or is it primarily classification and extraction? How many concurrent users will the system serve? What is the acceptable response latency? What languages must the model handle fluently? These questions constrain your search space far more usefully than any leaderboard.

Model size tiers: 7–8B, 32–70B, and 405B+

The open-source model landscape has consolidated around three practical size tiers. Models in the 7–8B parameter range — such as Mistral 7B, Llama 3.1 8B, and Qwen2.5 7B — are remarkably capable for focused tasks: document classification, extraction, summarisation, and FAQ-style question answering over a retrieval corpus. They run comfortably on a single consumer or prosumer GPU and deliver low latency even without heavy optimisation. The 32–70B tier — Llama 3.3 70B, Qwen2.5 32B, Mixtral 8x7B — is where general-purpose reasoning, multilingual fluency, and instruction-following quality improve substantially. These models can handle complex analytical tasks, longer contexts, and more nuanced generation. They require professional-grade GPUs but remain achievable for a single-server deployment. Above 70B, models like Llama 3.1 405B deliver frontier-level capability but demand multi-GPU setups and careful infrastructure planning; they are best reserved for use cases where quality is the primary constraint and budget is not.

• 7–8B models: best for focused, high-throughput tasks — classification, extraction, RAG over structured data. Single GPU, lowest cost.
• 32–70B models: strong general reasoning, multilingual support, longer contexts. Single high-end GPU or small multi-GPU node.
• 405B+ models: frontier quality for the most demanding tasks. Multi-GPU required; plan infrastructure carefully.
• Mixture-of-experts (MoE) architectures (e.g. Mixtral) can deliver 70B-class quality at closer to 13B active-parameter cost — worth evaluating if throughput matters.

Matching models to GPUs: VRAM is the binding constraint

GPU VRAM is the primary constraint that determines which models you can run and at what speed. A model must fit into VRAM for inference — with additional headroom for the KV cache, which grows with context length and batch size. As a rough guide: a 7–8B model in 16-bit precision requires around 14–16 GB of VRAM; a 32B model needs approximately 64 GB; a 70B model needs around 140 GB. This is why a single 24 GB GPU (such as the NVIDIA RTX 3090 or 4090) is the natural home for 7–8B models, a 48 GB card (RTX 6000 Ada) or 80 GB A100/H100 covers the 32–70B range on a single card, and anything larger requires multi-GPU configurations with NVLink or InfiniBand interconnects.

Quantization: reaching beyond your VRAM budget

Quantization reduces model weight precision — from 16-bit floats to 8-bit integers (INT8) or 4-bit (GPTQ, AWQ, GGUF Q4) — dramatically reducing VRAM requirements. A 70B model quantized to 4-bit can fit in approximately 35–40 GB of VRAM, making it accessible on a dual 24 GB GPU setup. The quality tradeoff depends on the quantization method and the task: for most production use cases, INT8 is nearly lossless, and well-implemented 4-bit quantization preserves the majority of model quality for tasks that are not highly sensitive to subtle reasoning errors. Quantization is not a workaround — it is a first-class deployment strategy that Privonis routinely uses to maximise capability per euro of hardware budget.

The right question is not "which model is best?" but "which model is sufficient for this task, on the hardware budget we have?" Quantization closes the gap between the two answers more than most teams expect.

Benchmarking before buying: the evaluation-first approach

No benchmark substitutes for evaluating a model on your actual data and tasks. Before committing to hardware, Privonis recommends running a structured evaluation: define a representative set of inputs from your production use case, establish quality criteria (accuracy, format adherence, latency at your target batch size), and test two or three candidate models on rented cloud GPU instances. This costs a few hundred euros and typically takes a day or two. The result is an evidence-based hardware specification rather than a guess — and it often reveals that a smaller, faster model meets your needs, saving significant capital expenditure.

• Define evaluation inputs from real production data before choosing a model.
• Test on rented GPU capacity first — cloud instances for evaluation, on-premise for production.
• Measure what matters: task accuracy, p95 latency, tokens per second at your expected batch size.
• Consider fine-tuning a smaller model before scaling to a larger one — a fine-tuned 7B often outperforms a generic 70B on narrow tasks.
• Plan for the KV cache: longer contexts consume VRAM fast; benchmark at maximum expected context length.

How Privonis guides the selection process

Choosing the right model and hardware combination is one of the highest-leverage decisions in a private AI deployment. A well-matched stack delivers the quality you need at a cost that makes the business case clear; a poorly matched one either overspends on idle compute or underperforms on tasks that matter. Privonis brings hands-on experience selecting, quantizing, fine-tuning, and benchmarking open-source LLMs across a range of European enterprise use cases. We help you avoid the expensive trial-and-error cycle and arrive at a deployment configuration that is right-sized from the start — and that remains maintainable as models and your use cases evolve.