Which GPU for Fine-Tuning 70B Models? A Technical Guide

Understanding the memory footprint of a 70B model is the first step in hardware selection. In standard FP16 or BF16 precision, each parameter occupies 2 bytes. This means the model weights alone require 140GB of VRAM. However, fine-tuning introduces additional overhead that far exceeds this baseline. During training, you must account for gradients, optimizer states, and activations.

For a full fine-tuning run using the AdamW optimizer, the memory requirement is approximately 16 to 18 bytes per parameter. This includes 2 bytes for the weight, 2 bytes for the gradient, and 12 to 14 bytes for the optimizer states (master weights, momentum, and variance). For a 70B model, this totals roughly 1.12 TB to 1.26 TB of VRAM. This massive requirement necessitates a multi-node cluster, typically consisting of at least 16x A100 or H100 80GB GPUs using techniques like Fully Sharded Data Parallel (FSDP) or DeepSpeed ZeRO-3.

Even with Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA, the memory pressure remains high. LoRA freezes the base model weights but still requires them to be loaded in memory. While it significantly reduces the gradient and optimizer state footprint, you still need approximately 160GB to 200GB of VRAM to maintain a reasonable batch size and context length. This is why 70B models represent a 'threshold' where consumer hardware fails and enterprise orchestration becomes mandatory.

The choice between full fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) dictates your hardware budget. Full fine-tuning updates every parameter in the model, which is necessary for deep domain adaptation or teaching the model entirely new reasoning patterns. As established, this requires a massive GPU fabric. If your goal is to align a model to a specific brand voice or a narrow set of instructions, PEFT is the more efficient path.

LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA) are the dominant PEFT techniques. QLoRA, in particular, is a game-changer for 70B models. By quantizing the base model to 4-bit precision, the weight footprint drops from 140GB to approximately 35-40GB. This allows a 70B model to fit onto a single 48GB GPU (like the NVIDIA RTX 6000 Ada or L40S) or a single 80GB GPU (A100/H100) with enough headroom for activations and adapters.

However, there is a performance trade-off. While QLoRA is memory-efficient, the quantization and de-quantization steps can introduce a slight computational overhead. Furthermore, if you are working with long context windows (e.g., 32k tokens or more), the KV cache will consume significant VRAM, potentially pushing you back into a multi-GPU setup even with 4-bit quantization. Lyceum’s platform helps engineers navigate these trade-offs by predicting the memory footprint before the job starts, preventing the dreaded Out-of-Memory (OOM) errors that plague manual deployments.

The NVIDIA H100 (Hopper architecture) is currently the gold standard for fine-tuning 70B models. Compared to its predecessor, the A100, the H100 offers a 2-3x increase in training throughput for transformer-based models. This leap is primarily driven by the fourth-generation Tensor Cores and the new Transformer Engine, which can dynamically manage FP8 precision.

FP8 is particularly relevant for 70B models. By using 8-bit floating-point precision for both weights and activations during training, the H100 can double the effective throughput compared to 16-bit training without a significant loss in model accuracy. Additionally, the H100 SXM5 variant features 3.35 TB/s of memory bandwidth, which is crucial for feeding the massive matrix multiplications inherent in 70B architectures. When running a 70B fine-tuning job on an 8-GPU H100 node, the high-speed NVLink interconnect allows for seamless gradient synchronization, ensuring that the GPUs spend more time computing and less time waiting for data transfers.

For teams moving past the initial prototyping phase, the H100 is often the most cost-effective choice despite its higher hourly rate. Because it completes training jobs significantly faster than the A100, the Total Cost of Compute (TCC) is frequently lower. Lyceum optimizes this further by providing one-click PyTorch deployment on H100 clusters in Berlin and Zurich, ensuring that European teams can access this performance while maintaining strict data sovereignty.

While the H100 is faster, the NVIDIA A100 (Ampere architecture) remains a workhorse in the AI industry. For many teams, especially those just graduating from hyperscaler credits, the A100 offers a more accessible entry point for 70B fine-tuning. The 80GB version is essential; the 40GB variant is simply too small to handle 70B models efficiently, even with heavy quantization.

In a multi-GPU configuration, a cluster of 4x or 8x A100 80GB GPUs can comfortably handle LoRA fine-tuning of a 70B model. However, the A100 lacks the Transformer Engine and native FP8 support found in the Hopper architecture. This means you are limited to BF16 or INT8 training, which is slower and more memory-intensive. In benchmarks, an A100 cluster typically delivers about 130 tokens per second for 70B inference, whereas an H100 can reach 250-300 tokens per second.

The primary advantage of the A100 today is availability and price stability. If your fine-tuning job is not time-constrained, the A100 can be a viable option. However, engineers must be wary of the 'hidden' costs of slower hardware. Longer training times mean more hours billed and slower iteration cycles for your ML team. Lyceum’s auto hardware selection engine can compare these factors for you, determining if the performance gains of an H100 outweigh the lower hourly cost of an A100 for your specific workload.

The introduction of the NVIDIA Blackwell B200 GPU represents a paradigm shift for large-scale model training. With 192GB of HBM3e memory and 8 TB/s of bandwidth, a single B200 has more memory than two H100s combined. For 70B models, this means you can fit the entire FP16 model weights plus a substantial amount of activation memory on a single GPU, or run massive batch sizes across a small cluster.

Blackwell’s second-generation Transformer Engine supports FP4 precision, which can reduce the memory footprint of a 70B model even further than QLoRA, while maintaining higher throughput. In MLPerf benchmarks, Blackwell systems have shown a 2.2x performance boost over Hopper for Llama 2 70B fine-tuning. This is not just an incremental improvement; it allows for the fine-tuning of even larger models (like the 405B variant) that were previously the exclusive domain of massive research labs.

For European enterprises, Lyceum is at the forefront of this transition, integrating Blackwell GPUs into our sovereign cloud infrastructure. The B200’s efficiency is particularly beneficial for liquid-cooled data centers, which reduce the environmental impact of massive AI workloads. As 70B models become the baseline for sophisticated AI agents, the B200 will likely become the preferred choice for teams requiring the highest possible iteration speed and the ability to handle extremely long context windows without performance degradation.

When fine-tuning a 70B model across multiple GPUs, the speed at which those GPUs communicate is often the primary bottleneck. This is where the distinction between PCIe and NVLink becomes critical. Standard PCIe Gen5 x16 slots provide about 128 GB/s of bidirectional bandwidth. While this sounds fast, it is an order of magnitude slower than the internal memory bandwidth of the GPU itself.

NVLink is a dedicated GPU-to-GPU interconnect that bypasses the CPU and the PCIe bus. On H100 systems, NVLink 4.0 provides up to 900 GB/s of bandwidth per GPU. For Blackwell, NVLink 5.0 bumps this to 1.8 TB/s. In a 70B fine-tuning scenario using FSDP, the GPUs must constantly exchange gradients and optimizer states. If you are using a PCIe-based system, the GPUs will spend a significant portion of each training step idling while waiting for data to move across the bus. This results in poor scaling; adding more GPUs may not lead to a linear increase in training speed.

For any 70B fine-tuning project, we strongly recommend using SXM-based servers (which utilize NVLink) rather than PCIe-based cards. The performance difference can be as high as 2-3x in multi-GPU setups. Lyceum’s infrastructure is built on these high-speed fabrics, ensuring that your distributed training jobs scale efficiently. We eliminate the complexity of configuring these interconnects, providing a pre-optimized environment where NVLink and InfiniBand are ready out of the box.

Is it possible to fine-tune a 70B model on a single GPU? The answer is yes, but with caveats. Using 4-bit QLoRA, the model weights take up about 35GB. On an 80GB A100 or H100, this leaves 45GB for activations, gradients, and the KV cache. This is sufficient for short-context fine-tuning with small batch sizes. However, if you need to process long documents or use larger batches to stabilize training, you will quickly hit the 80GB limit.

A more robust 'budget' setup for 70B models is a dual-GPU configuration, such as 2x RTX 6000 Ada (48GB each) or 2x A100 (80GB each). By using the Accelerate library or DeepSpeed, you can shard the model across both cards. This provides a total of 96GB to 160GB of VRAM, which is the 'sweet spot' for QLoRA fine-tuning. It allows for a context length of 4k to 8k tokens and a batch size that ensures the model actually learns the desired patterns effectively.

For teams without the budget for an 8-GPU H100 node, these smaller multi-GPU setups are highly effective. The key is orchestration. Managing memory across two GPUs requires careful placement of model layers and careful monitoring of fragmentation. Lyceum’s VS Code extension and CLI tool simplify this by automatically detecting memory bottlenecks and suggesting the optimal hardware configuration for your specific QLoRA parameters, ensuring you don't overpay for compute you don't need.

In the world of AI infrastructure, the hourly rate of a GPU is a misleading metric. What actually matters is the Total Cost of Compute (TCC)—the total amount spent to reach a specific training milestone. A 'cheap' GPU that takes three times longer to complete a job is ultimately more expensive than a high-performance card. This is especially true for 70B models, where training runs can last for days.

Lyceum addresses this by moving away from generic cloud pricing toward workload-aware pricing. Our platform analyzes your PyTorch code and predicts the runtime and utilization before the job even starts. If our engine determines that an H100 cluster will complete your 70B fine-tuning job in 10 hours while an A100 cluster would take 30 hours, we provide that data upfront. This allows CTOs to make informed decisions based on both budget and time-to-market constraints.

Furthermore, Lyceum eliminates hidden costs like egress fees. In traditional hyperscaler environments, moving large 70B model checkpoints out of the cloud can result in massive, unexpected bills. With Lyceum, your data stays within our EU-sovereign regions (Berlin and Zurich), and there are zero egress fees. This transparency, combined with our auto-hardware selection, ensures that your AI team can focus on model performance rather than infrastructure accounting.

Fine-tuning a 70B model often involves sensitive proprietary data or regulated customer information. For European companies, this creates a compliance challenge: how to access high-performance GPUs without sending data to jurisdictions with weaker privacy protections. Most major cloud providers are subject to the US CLOUD Act, which can conflict with GDPR requirements for data residency.

Lyceum is GDPR-compliant by design. Our GPU clusters are located exclusively in Berlin and Zurich, and our platform is built to ensure that your data never leaves the European Union. This sovereignty extends to the entire stack, from the physical hardware to the orchestration layer. When you fine-tune a 70B model on Lyceum, you are using a platform that understands the legal and technical requirements of the European market.

This focus on sovereignty does not come at the expense of performance. By providing one-click access to the latest NVIDIA hardware within a compliant framework, Lyceum enables European scaleups to compete on a global level. Whether you are building a specialized legal LLM or a medical diagnostic tool, you can train your 70B models with the confidence that your intellectual property and your customers' data are protected by the highest standards of European law.