How Much VRAM for a 70B Model? A Technical Engineering Guide

To understand how much VRAM a 70B model requires, we must first look at the raw parameter count. A 70 billion parameter model consists of 70 billion individual weights. In standard half-precision (FP16 or BF16), each parameter occupies 2 bytes of memory. The baseline calculation is straightforward: 70,000,000,000 parameters multiplied by 2 bytes equals 140,000,000,000 bytes, or approximately 130.39 GiB. This is the absolute minimum VRAM required just to load the model weights into memory without accounting for any activations, KV cache, or system overhead.

In a production environment, you cannot simply provision 140GB of VRAM and expect the model to run. CUDA kernels, library overheads, and the operating system itself consume a portion of the available memory. Furthermore, the model architecture dictates how these weights are distributed. For a 70B model, this usually necessitates a multi-GPU setup, such as two NVIDIA A100 80GB cards or two H100 80GB cards. When using multiple GPUs, communication buffers for technologies like NCCL (NVIDIA Collective Communications Library) also add to the memory footprint. Engineers must account for a safety margin of at least 10 to 15 percent above the raw weight size to ensure stability during inference. Without this buffer, even a small increase in input sequence length can trigger an immediate OOM error, crashing the entire inference service.

Quantization is the most effective technique for reducing the VRAM footprint of 70B models. By reducing the precision of the weights from 16-bit to 8-bit or 4-bit, you can significantly lower the entry barrier for hardware. In 8-bit quantization (INT8), each parameter uses 1 byte, bringing the model weight size down to approximately 70GB. This allows a 70B model to fit onto a single 80GB GPU, though with very limited room for context. The real breakthrough for many teams comes with 4-bit quantization techniques like GPTQ, AWQ, or GGUF.

At 4-bit precision, each parameter occupies roughly 0.5 bytes. However, due to the need for quantization constants and metadata, the actual footprint is closer to 0.7 to 0.8 bytes per parameter. For a 70B model, 4-bit quantization results in a weight size of approximately 40GB to 48GB. This makes it possible to run a 70B model on a single NVIDIA L40S (48GB) or even a dual RTX 4090 (24GB x 2) setup. While there is a slight degradation in perplexity when moving from FP16 to 4-bit, the trade-off is often worth it for the massive reduction in infrastructure costs. Lyceum Technologies simplifies this by providing workload-aware pricing and auto-selecting hardware that matches the specific quantization level of your deployment, ensuring you do not overpay for unused VRAM capacity.

Loading the weights is only half the battle. During inference, the model generates a Key-Value (KV) cache to store the hidden states of previous tokens in a sequence. This prevents the model from recomputing the entire sequence for every new token generated, which is essential for performance. The size of the KV cache grows linearly with the sequence length and the number of concurrent requests (batch size). For a 70B model with a large context window, the KV cache can quickly consume tens of gigabytes of VRAM.

The formula for KV cache size is roughly: 2 * layers * heads * head_dim * sequence_length * num_beams * batch_size * bytes_per_param. For a model like Llama 3 70B, which has 80 layers and a hidden dimension of 8192, a context length of 8,192 tokens in FP16 precision can take up significant space. If you are targeting long-context applications (e.g., 32k or 128k tokens), the KV cache might actually exceed the size of the quantized model weights. Engineers must use techniques like PagedAttention (implemented in vLLM) to manage this memory more efficiently. PagedAttention reduces fragmentation by allocating KV cache in small blocks, similar to virtual memory in operating systems, which can improve GPU utilization from the industry average of 40 percent to much higher levels.

Fine-tuning a 70B model is an entirely different challenge compared to inference. During training, the GPU must store not only the model weights but also the gradients, optimizer states, and forward activations. If you are using the Adam optimizer in FP16, each parameter requires 2 bytes for the weight, 2 bytes for the gradient, and 12 bytes for the optimizer states (momentum and variance in FP32). This totals 16 bytes per parameter. For a 70B model, full fine-tuning requires approximately 1.12 terabytes of VRAM.

This level of memory requirement necessitates a massive cluster of GPUs, typically at least 16 to 20 A100 80GB or H100 80GB cards, connected via high-speed interconnects like NVLink. Even with DeepSpeed ZeRO-3 redundancy reduction, which shards the optimizer states and gradients across all available GPUs, the aggregate VRAM needed remains the same. For most scaleups and mid-market companies, full fine-tuning is economically unfeasible without specialized orchestration. Lyceum Technologies addresses this by offering EU-sovereign GPU clouds in Berlin and Zurich, providing the high-performance compute needed for these workloads without the egress fees or data residency concerns associated with US-based hyperscalers.

Parameter-Efficient Fine-Tuning (PEFT) techniques, specifically LoRA (Low-Rank Adaptation) and QLoRA, have revolutionized the accessibility of 70B models. LoRA works by freezing the original model weights and only training a small number of adapter weights. This drastically reduces the memory needed for gradients and optimizer states. QLoRA takes this a step further by quantizing the base model to 4-bit (using a special NormalFloat4 data type) and using paged optimizers to handle memory spikes.

With QLoRA, the VRAM requirement for the base 70B model drops to about 35-40GB. The additional memory needed for the adapters and activations depends on the rank (r) and alpha settings, but it typically adds only a few gigabytes. This allows a 70B model to be fine-tuned on a single 80GB GPU or a small cluster of 48GB GPUs. This is a game-changer for AI teams that need to specialize a large model on proprietary data while keeping costs under control. When deploying these jobs, Lyceum's platform can automatically detect memory bottlenecks and suggest the optimal hardware configuration, ensuring that your QLoRA job doesn't fail halfway through a multi-day run due to an unexpected activation spike.

When a model exceeds the VRAM of a single GPU, you must employ parallelism strategies. The two most common are Pipeline Parallelism (PP) and Tensor Parallelism (TP). Pipeline Parallelism splits the model layers across different GPUs. For example, in a 2-GPU setup, layers 1-40 might live on GPU 0, and layers 41-80 on GPU 1. While this is simple to implement, it can lead to GPU idle time (bubbles) as one GPU waits for the other to finish its computation. Tensor Parallelism is more complex but more efficient, as it splits individual weight matrices across GPUs, allowing them to work on the same layer simultaneously.

For a 70B model in FP16, you would typically use a 2-way or 4-way Tensor Parallelism setup. This requires high-bandwidth interconnects like NVLink to minimize the communication overhead between GPUs. If you are using a cloud provider without high-speed interconnects, the latency of moving data over PCIe can negate the benefits of multi-GPU scaling. Lyceum's infrastructure is designed for these high-performance workloads, offering one-click PyTorch deployment that handles the underlying complexity of sharding and communication. This allows ML engineers to focus on model architecture rather than the intricacies of CUDA stream synchronization and NCCL collective operations.

One of the most common frustrations for AI teams is the 'trial and error' approach to provisioning GPUs. You launch a job, wait for it to initialize, and then watch it crash with a 'CUDA Out of Memory' error five minutes later. This waste of time and resources is exactly what Lyceum Technologies aims to eliminate. By providing precise predictions of runtime, memory footprint, and utilization before jobs even run, engineers can select the right hardware from the start. This is particularly important for 70B models where the difference between a 40GB and 80GB GPU is significant in terms of cost.

Predicting memory usage involves analyzing the model's computational graph and accounting for the specific framework overheads (PyTorch vs. JAX). For instance, PyTorch's caching allocator might hold onto memory that is technically free, leading to reported usage that is higher than actual requirements. Lyceum's orchestration layer accounts for these nuances, offering a 'Total Cost of Compute' (TCC) model that reflects actual resource consumption. This workload-aware pricing ensures that if your 70B model only utilizes 60 percent of an A100's VRAM due to efficient quantization, your costs are optimized accordingly, rather than being billed for the maximum theoretical capacity of the hardware.

For European companies, the decision of where to host a 70B model is not just a technical one, but a legal one. Large language models are often fine-tuned on sensitive internal data, including PII or proprietary intellectual property. Using US-based hyperscalers can introduce compliance risks under GDPR, especially when data is processed in regions without equivalent privacy protections. Lyceum Technologies provides an EU-sovereign alternative with data centers located in Berlin and Zurich. This ensures that your data never leaves the European Union, providing a 'GDPR by design' infrastructure for your most sensitive AI projects.

Furthermore, Lyceum eliminates the hidden costs often associated with large-scale AI, such as egress fees. When working with 70B models, moving model checkpoints (which can be 140GB each) or large datasets between regions can result in thousands of euros in unexpected charges on platforms like AWS or GCP. By removing these fees and focusing on a transparent, workload-aware pricing model, Lyceum enables European scaleups to compete globally while maintaining strict adherence to local data residency requirements. This combination of high-performance hardware, automated orchestration, and sovereign compliance makes it the ideal platform for the next generation of European AI innovation.