What Models you can Actually Run on your Hardware

One of the biggest optimization problems that nobody talks about is determining what ML models your hardware is capable of running and at what performance thresholds. Sure, you may be able to fit a 30B parameter model on your GPU but if your token throughput caps at 5/sec and your request latency is above 10000ms is it really hostable?

This is where the core of the issue lies, and why the answer to the problem itself is not straightforward. In order to answer the question, you need to have hard KPIs in place that will tell you definitively whether or not your model choice is viable.

Before even assessing model performance we must first see if the model itself can even fit on our hardware. For the sake of this article, let's assume we are running with a singular NVIDIA L4 GPU. The important specs to consider at this step are:

The first parameter to assess here is the total GPU Memory. This effectively defines the total amount of space your model has for its parameters, KV cache, and overhead. If your proposed model doesn't fit in memory then there are optimizations you can make to improve performance; you simply cannot run that model.

In most cases the model weights themselves eat the majority of the required memory. For context, a model like Llama 3.1:8B has 8 Billion total parameters. By default, these are typically storing at FP16, meaning each value is represented using 16 bits (similarly 32 for FP32, and 8 for FP8).

Doing some quick math, we quickly see how this adds up. 8 Billion parameters * 16 bits:

Without employing any optimization techniques, the model weights themselves already take up 66% of total allocatable space.

At this point, many people stop their analysis after confirming that the model weights fit in VRAM. This is where most deployment attempts quietly fail. Model weights are only part of the memory story. The other two major consumers are the KV cache and runtime overhead.

The KV cache stores key-value tensors for every token processed during inference. This allows transformers to avoid recomputing attention for previous tokens, but it comes at a steep memory cost that scales with:

This is why a model that technically fits at idle can immediately OOM the moment you increase context length or concurrency. KV cache growth is linear with tokens and multiplicative with parallel requests.

On top of this, frameworks like vLLM, TensorRT-LLM, or Hugging Face Accelerate introduce their own runtime buffers, CUDA graphs, and allocator overhead. Conservatively, you should reserve 10–20% of total VRAM for non-model usage.

Parameter count is often treated as the primary indicator of model feasibility, but it is an incomplete and sometimes misleading metric. Two models with identical parameter counts can have drastically different runtime characteristics.

Architectural choices such as grouped-query attention, hidden dimension width, and attention head count materially affect memory bandwidth pressure and compute efficiency. This is why some 7B models outperform certain 13B models on identical hardware.

In practice, what matters is not how large the model is, but how efficiently your hardware can move data through it.

Once a model fits in memory, the next question is performance. Specifically, two KPIs determine whether a model is actually usable: token throughput and end-to-end latency.

High throughput with poor latency is unacceptable for interactive workloads. Conversely, low throughput with excellent latency collapses under concurrency. You must define which metric matters for your use case before selecting a model.

This is where many large models fail on mid-tier GPUs. They may generate tokens, but at a rate that renders them impractical.

GPU compute is rarely the limiting factor for inference. Memory bandwidth is. Transformer inference is fundamentally a data movement problem, not a raw FLOPs problem.

On an NVIDIA L4, you have approximately 300GB/s of memory bandwidth. Every token generation step requires reading large weight matrices from VRAM. If your model saturates memory bandwidth, adding more compute capacity does nothing.

This is why quantization often yields dramatic speedups even when compute utilization appears low. Reducing weight size reduces memory traffic, directly improving throughput.

If your model is borderline viable, there are only a handful of optimizations that meaningfully change the outcome:

What does not help is blindly scaling batch size or enabling every optimization flag without understanding the tradeoffs. Most performance regressions come from over-allocating KV cache or misaligned batching strategies.

Rather than asking “Can my GPU run this model?”, the correct question is:

A practical evaluation flow looks like this:

If a model fails at any step, it is not a hardware problem — it is a model selection problem.

The gap between “can technically run” and “can responsibly host” is vast. Treating model deployment as a binary decision leads to poor system design and disappointing results.

By grounding model choice in memory math, bandwidth realities, and concrete performance KPIs, you can make informed decisions that scale predictably instead of optimistically.