GPUYard

  Training 70B+ parameter models often hits a hard memory wall. Standard 8x80GB GPU nodes run out of memory (OOM) quickly when handling parameters, gradients, and optimizer states. While PyTorch FSDP1 helped bridge the gap, its FlatParameter architecture made memory allocation unpredictable and LoRA fine-tuning complex. Enter PyTorch FSDP2 (DTensor Architecture) PyTorch FSDP2 shifts from flat buffers to DTensor-based per-parameter sharding . This ensures that memory usage scales linearly across your GPU mesh. Key Upgrades in FSDP2: Predictable Memory: Eliminates unexpected OOM spikes during gradient syncing. Seamless LoRA Support: Works out-of-the-box by setting requires_grad=False . Native Distributed Checkpointing (DCP): Prevents OOM crashes during saves by eliminating the need to gather full parameters onto a single rank. Below is a quick comparison of how FSDP1 and FSDP2 handle multi-node workloads: Feature FSDP1 (FlatParameter) FSDP2 (DTensor) Sharding Mechanism Flattens w...

  VRAM—not raw compute—is what actually kills most self-hosted LLM projects. Running a 70B parameter model in standard FP16 requires expensive multi-GPU clusters. In our latest technical guide on GPUYard, we walk through deploying Llama 3 using NVIDIA Blackwell's native NVFP4 precision with TensorRT-LLM on single workstation cards like the RTX 5090 and RTX PRO 6000. What you'll learn in the full guide: Why you don't need a enterprise B200 cluster Fine-grained E4M3 scaling vs. traditional INT4 quantization Complete 6-step command line walkthrough (Docker, ModelOpt, TRT-LLM engine build) 👉 Read the Full Step-by-Step Deployment Guide on GPUYard

  Deploying Large Language Models (LLMs) like Llama 3 in production requires balancing speed, memory, and infrastructure cost. To extract maximum ROI from high-performance GPUs, AI development teams are combining NVIDIA H100 & RTX Pro 6000 Ada hardware with NVIDIA's TensorRT-LLM optimization engine. If you are looking to scale your AI backend, here is a quick breakdown of how TensorRT-LLM optimizes inference and how to choose the right hardware for your workload. ⚡ Why TensorRT-LLM Changes the Game Serving raw models with default setups wastes GPU memory and compute power. TensorRT-LLM solves this through three core features: Native FP8 Precision: Both H100 (Hopper) and RTX Pro 6000 (Ada Lovelace) feature 4th-generation Tensor Cores. Quantizing weights to FP8 cuts memory usage in half while maintaining model accuracy—leaving more VRAM for massive user concurrency. In-Flight Continuous Batching: Instead of waiting for an entire batch of requests to finish, new requests are d...

  If you are running Large Language Models (LLMs) in production, you have probably hit a wall where your GPUs run out of memory (OOM) much faster than expected. Most engineering teams size their GPUs based on the model weights (~140 GB for a 70B parameter model in FP16) and assume the rest of the VRAM is just headroom. In production, that assumption breaks down fast. The silent killer of LLM deployments isn't the model, it is the KV Cache . The Shocking Reality of Long-Context VRAM Every time an LLM processes a token, it stores the Key and Value tensors in the KV cache so it doesn't have to recompute them. For long-running AI agents, this grows linearly and massively. Let’s look at a real-world example using Llama 2 70B at a 32K context window. Here is the exact VRAM footprint for just one single user : For a single sequence at 32,768 tokens (using Grouped-Query Attention and FP16): Memory Consumed: 10,737,418,240 bytes Total: ~10.74 GB per sequence That is almost 11 GB of ...