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 ...

  If you are running RAG pipelines, AI agents, or multi-turn chatbots on a dedicated GPU server, standard LLM serving engines are wasting your VRAM. In a standard setup, the key-value (KV) cache is discarded after every request. If your agent sends the same massive system prompt over and over, the engine recomputes it from scratch every time. Enter SGLang and RadixAttention. SGLang (developed out of UC Berkeley) solves this by organizing all cached KV states into a radix tree. It automatically finds the longest matching prefix and only computes attention for the new tokens. This drastically reduces the Time-to-First-Token (TTFT) for any workload with repeated context. Hardware VRAM Rule of Thumb for SGLang: Before deploying, you need to ensure your server has the right baseline: 8B Models (Llama 3.1): 16–24 GB VRAM (RTX 4090, A100 40GB) 32B Models: 48–64 GB VRAM (A100 80GB, H100) 70B Models: 140+ GB VRAM (Requires Tensor Parallelism across multiple GPUs) How to Deploy It Get...

  Most AI teams provision GPUs the way they provision servers: one workload, one full device. But running a 7B-parameter inference endpoint or a batch embedding job doesn't require a massive 80GB of HBM3 memory. When you run lightweight workloads on a full A100 or H100, most of the silicon sits idle—while your budget burns away. NVIDIA’s Multi-Instance GPU (MIG) technology solves this by physically dividing a single GPU into up to 7 independent, hardware-isolated instances. Here is a quick breakdown of how it works and how you can implement it to maximize your hardware ROI. Why Choose MIG Over Time-Slicing? Unlike software-based sharing methods (like CUDA MPS or time-slicing) where processes cooperatively share resources, MIG offers true hardware-level isolation . Dedicated Resources: Each MIG instance gets its own assigned Streaming Multiprocessors (SMs), memory, and L2 cache. Zero Resource Contention: A massive, memory-heavy request on one instance cannot starve, slow down, or...

  Every millisecond between a user's request and your AI model's response is a design decision. For live applications like chatbots, recommendation engines, or real-time fraud detection network latency is often the difference between a product that feels instant and one that feels broken. If your GPU infrastructure sits in the wrong place, you are fighting a losing battle against physics. Here is what you need to know about optimizing AI inference for the UK and Europe. 1. Training Latency vs. Inference Latency Are Not the Same It is easy to lump "AI performance" into one bucket, but they have completely different tolerances for delay: Training jobs running for 12 hours do not care if a data batch takes an extra 200 milliseconds to load. Live inference is synchronous. A user is actively waiting on the other end. If your pipeline involves multiple steps (API gateway → database context → GPU compute → response), a poorly optimized network will ruin the user experience...