How to Manage GPU Workloads in Cloud Environments

“Just rent a GPU for training” Until you need: - Multi-node training for 70B+ models - $5/hour per GPU (not $30/hour) - 90%+ GPU utilization Then you build your own ml infra. Here’s the reality: Most ML engineers think training infrastructure = - Rent some A100s - Install PyTorch - Run training script - Scale with more GPUs The pain starts around 8 GPUs. Remember: You’re not training ONE model on ONE GPU. You’re orchestrating DOZENS of experiments across hundreds of GPUs with checkpointing, fault tolerance, and resource sharing. That’s a scheduling problem, not a training problem. What you actually need: > Job scheduler that understands GPU topology > Distributed checkpoint manager that doesn’t waste bandwidth > Network fabric optimized for all-reduce > Elastic training that handles node failures This is the actual platform. Your training cost breakdown at scale: > Compute: $10/GPU-hour (you pay $30 on cloud) > Data transfer: $2/TB (kills you with large datasets) > Storage: $0.02/GB-month (checkpoints add up fast) > Network: Included (but becomes bottleneck) The hidden cost? Idle GPU time while debugging. The first principle of distributed training: Bandwidth >> Compute for models over 10B params Ring all-reduce needs 2(N-1)/N bandwidth efficiency. With 64 GPUs on 3.2 Tbps InfiniBand, you max out at 200GB/sec actual throughput. This is why “just add more GPUs” plateaus. Training Llama 70B: - 140GB model weights - Optimizer states: 280GB - Checkpoints every 1K steps - 30 checkpoints = 12.6TB One training run = $250 in storage. You run 50 experiments/month. “We need to train 10 models simultaneously with different hyperparameters” Now your platform needs: > Gang scheduling for multi-GPU jobs > Spot instance preemption handling > Shared dataset caching across jobs > Priority queues with fairness 90% of DIY platforms can’t do this. > Use cloud when you’re training <5 models/month, using standard frameworks, can tolerate random failures, and engineering time costs more than GPU markup. > Build your own when you train 20+ models/month, need 70B+ params, want <$10/GPU-hour, or are spending $50K+/month. The actual math: AWS p5.48xlarge (8× H100): $98/hour 100 training runs × 48 hours = $470,400/year Your bare-metal with 64× H100s at $2.5M upfront: Depreciation + power = $150K/year at 60% utilization = $312,500 Plus $200K engineer, $50K maintenance. Break-even: 18 months. Production training platforms have four layers: - Orchestration (job queue, gang scheduler, resource manager). - Execution (distributed runtime, checkpoint manager, fault handler). - Storage (dataset cache, checkpoint store, artifact registry). - Telemetry (GPU util, training metrics, cost per epoch). Most build layer 2, skip the rest. That’s it. Building training infrastructure is a 9-month project with upfront hardware costs. But at 100+ training runs/month? ROI in 12 months. #ml #gpu #llm #infra #cloud #nvidia #inference #aws #cloud #ai

⚙️ 𝗚𝗣𝗨𝘀 𝗮𝗿𝗲𝗻’𝘁 𝘁𝗵𝗲 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝗶𝘀 A lot of teams running shared GPU clusters are hitting the same wall. Not because they can’t get GPUs, but because 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗔𝗜 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 𝗼𝗻 𝗯𝗮𝗿𝗲 𝗺𝗲𝘁𝗮𝗹 𝗶𝘀 𝗵𝗮𝗿𝗱. The real pain shows up as: • Unfair GPU allocation across teams • Weak isolation between workloads • Manual, ticket-driven environment setup • Expensive GPUs sitting idle As AI and ML workloads move from experiments to production systems, these problems get amplified on bare-metal GPU infrastructure. Recently, I worked on this 𝘂𝗻𝗴𝗮𝘁𝗲𝗱 𝗴𝘂𝗶𝗱𝗲 that breaks down how to build a 𝗰𝗹𝗼𝘂𝗱-𝗹𝗶𝗸𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 on bare metal GPUs using vCluster It covers how to enable: ✨ Strong multi-tenancy and isolation ✨ Self-service environments for ML teams ✨ Faster onboarding without central bottlenecks ✨ Higher GPU utilization with lower operational overhead If you’re operating shared GPU infrastructure and want it to behave more like an internal cloud platform, this should be useful 👇 🔗 https://lnkd.in/e7aDZ4vQ

Designing an AI System That Doesn’t Collapse Under Latency Spikes A single user query passes through multiple stages — tokenization → batching → GPU scheduling → model execution → post-processing → response assembly. Now picture this: A few heavy prompts take 5× longer than average. Your batching layer waits to fill the “perfect batch.” Meanwhile, the queue grows. Requests start timing out. Retries stack up. That’s when you realize: You’re not running out of compute. You’re running out of control. Here’s how you design for resilience instead of collapse 👇 1️⃣ Bounded Queues Never let latency scale linearly with load. Bound your input queues and shed load proactively — either by dropping excess requests or serving degraded responses. Unbounded queues are silent killers — they delay backpressure, causing cascading timeouts. Think of it like circuit breakers for inference — graceful denial is better than system-wide collapse. 2️⃣ Adaptive Batching Static batch sizes look great in benchmarks and terrible in production. Instead, make batch sizes dynamic — continuously tuned based on GPU occupancy, queue length, and recent tail latency percentiles (P95/P99). At low load, batch small for lower latency. At high load, batch large for throughput — but with strict timeouts. The goal is elasticity without unpredictability. 3️⃣ Token-Aware Scheduling Batching by request count is naive. In LLM workloads, token length determines cost. A single 10,000-token prompt can stall 15 smaller ones if batched together. Token-aware schedulers measure total token budget per batch and allocate GPU time accordingly. This ensures fairness and consistent latency curves even under mixed workloads. 4️⃣ Partial Caching Most engineers cache final model outputs. That helps little. What actually saves time is pre- and post-compute caching — tokenized inputs, embeddings, and prompt templates. These are deterministic and cheap to reuse, shaving milliseconds off critical paths. Combine that with vector cache lookups to skip redundant reasoning altogether. 5️⃣ Deadline-First Scheduling In multi-tenant inference systems, not all requests are equal. Prioritize requests based on expected completion deadlines instead of FIFO order. This minimizes tail latency and improves QoS across traffic tiers. It’s the same principle airlines use — business class boards first, but everyone still gets there. This is where systems engineering meets AI infrastructure. Because LLM inference at scale isn’t just about throughput — it’s about temporal predictability. Inside my Advanced System Design Cohort, we go deep into these challenges — how to design AI systems that don’t just scale, but stay stable under load. If you’ve been leading distributed systems or AI infra and want to sharpen your architectural depth, there’s a link to a form in the comments — apply, and we’ll check if you’re a great fit.

After training 𝐒𝐦𝐨𝐥𝐋𝐌𝟑 on 𝟑𝟖𝟒 𝐇𝟏𝟎𝟎𝐬 for nearly a month, I've come to realize something most people overlook: 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐦𝐚𝐤𝐞-𝐨𝐫-𝐛𝐫𝐞𝐚𝐤 𝐟𝐚𝐜𝐭𝐨𝐫 𝐢𝐧 𝐋𝐋𝐌 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠. 🔥 Everyone talks about model architecture and data quality. And yes, those matter immensely. But here's what nobody tells you: when your training run fails at 2 AM because of mysterious 𝐍𝐂𝐂𝐋 𝐞𝐫𝐫𝐨𝐫𝐬, or when your expensive GPU cluster is running at 𝟔𝟎% 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲, the problem isn't your model. It's most probably a 𝐦𝐢𝐬𝐮𝐬𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐡𝐚𝐫𝐝𝐰𝐚𝐫𝐞. Questions that seemed simple but had no clear answers: Why is 𝐌𝐨𝐄 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐬𝐥𝐨𝐰𝐞𝐫 𝐭𝐡𝐚𝐧 𝐝𝐞𝐧𝐬𝐞 𝐦𝐨𝐝𝐞𝐥𝐬? Which 𝐍𝐂𝐂𝐋 𝐟𝐥𝐚𝐠𝐬 should we actually set? How often should we checkpoint without killing throughput? That's why we built 𝐓𝐡𝐞 𝐒𝐦𝐨𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤 📖: a complete guide covering everything from model architecture and data curation to the SmolLM3 training marathon, post-training techniques, and crucially, the 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐥𝐚𝐲𝐞𝐫 that most teams get wrong. Here's what surprised us most: 𝘪𝘯𝘵𝘦𝘳𝘤𝘰𝘯𝘯𝘦𝘤𝘵 𝘵𝘰𝘱𝘰𝘭𝘰𝘨𝘺 𝘪𝘴 𝘢𝘭𝘮𝘰𝘴𝘵 𝘢𝘭𝘸𝘢𝘺𝘴 𝘮𝘪𝘴𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘰𝘰𝘥, and wrong configurations can silently destroy your GPU-to-GPU bandwidth. We spent weeks validating every layer of our AWS p5 system, and the results were eye-opening. 👀 We validated real vs theoretical bandwidth across the entire stack: 𝐇𝐁𝐌𝟑 𝐡𝐢𝐭𝐭𝐢𝐧𝐠 𝟑 𝐓𝐁/𝐬, 𝐍𝐕𝐋𝐢𝐧𝐤 𝟒.𝟎 𝐫𝐞𝐚𝐜𝐡𝐢𝐧𝐠 𝟕𝟖𝟔 𝐆𝐁/𝐬, 𝐏𝐂𝐈𝐞 𝐆𝐞𝐧𝟒 𝐚𝐭 𝟏𝟒.𝟐 𝐆𝐁/𝐬. Then we ran collective operations across 𝟏𝟐𝟖 𝐆𝐏𝐔𝐬 (16 nodes, 8xH100s each) and measured how performance degrades at scale: all-reduce drops from 𝟒𝟖𝟎 𝐆𝐁/𝐬 on a single node to 𝟑𝟐𝟎-𝟑𝟓𝟎 𝐆𝐁/𝐬 across 16 nodes. The good news? Once you understand what's happening, you can fix it. We documented everything: bandwidth measurements, annotated topology diagrams, troubleshooting workflows. And listed the tools you can use: 𝐧𝐯𝐛𝐚𝐧𝐝𝐰𝐢𝐝𝐭𝐡 for measuring communication paths, 𝐍𝐒𝐢𝐠𝐡𝐭 𝐂𝐨𝐦𝐩𝐮𝐭𝐞 for roofline analysis, step-by-step guides for debugging your specific setup. Infrastructure shouldn't be this invisible layer that only a handful of experts understand. When you can 𝐦𝐞𝐚𝐬𝐮𝐫𝐞, 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐞, 𝐚𝐧𝐝 𝐝𝐞𝐛𝐮𝐠 𝐢𝐭 𝐩𝐫𝐨𝐩𝐞𝐫𝐥𝐲, suddenly those mysterious slowdowns become solvable problems. 🚀 If you've ever wondered why your training runs are slower than they should be, or you're planning to scale up and want to avoid expensive mistakes, this guide might save you weeks of debugging. 𝐓𝐡𝐞 𝐒𝐦𝐨𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤: https://lnkd.in/e5MKXUHS Shared with ❤️ by the HuggingFace team

You’re in an ML Engineer interview at Google. The interviewer asks: “Your API traffic spikes 10x during peak hours. How do you scale inference infra without burning cash?” Most answers? “Add more GPUs.” ❌ Wrong. That’s how you bankrupt the company. ⸻ The real problem: • LLMs don’t scale like web servers. • Spinning up GPUs takes minutes, not milliseconds. • Idle GPUs = thousands of dollars wasted per day. ⸻ Levels of thinking: • Junior: Provision for peak load (most infra sits idle). • Senior: Autoscale GPU clusters with warm pools. • Principal: Predictive scaling using traffic analytics + quantized backup models. ⸻ Real-world techniques: • GPU warm pools: Keep a few standby GPUs hot, ready to accept jobs. • Spot instances / preemptibles: Cut costs for non-critical inference jobs. • Model quantization: Serve lighter versions during peak load to save memory. • Speculative decoding: Cut latency so fewer GPUs are needed per request. • Hybrid scaling: Mix GPU + CPU inference for lightweight requests. ⸻ Follow-up question: “How do you scale down without dropping active sessions?” ✅ Answer: Graceful draining — finish current requests, migrate caches, then release GPU. Bottom line: Autoscaling LLMs is not “just Kubernetes.” It’s about prediction, warm starts, and smart fallbacks. #llm #scaling #cloud #machinelearning #ai

You're in a Senior ML Engineer interview at NVIDIA and the interviewer asks: "You just migrated your team's deep learning workloads from local hardware to a massive AWS GPU cluster to accelerate training. The expensive instances are successfully spinning, but your training iteration speed has actually flatlined. What is the hidden system bottleneck throttling your pipeline?" Don't say: "It's a network latency issue. We just need to pay for a higher-bandwidth VPC or upgrade to faster compute instances." Wrong approach. You're just throwing more cloud budget at the wrong problem. The reality is that scaling up cloud compute almost always exposes the severe I/O Starvation of your data pipeline. You've essentially bought a fleet of Ferraris, but you're trying to fuel them through a garden hose. When you train locally, your data is likely sitting on a hyper-fast local NVMe drive. When you move to the cloud, your data is usually dumped into object storage (like AWS S3 or GCS). Here is what is actually killing your training speed: 1️⃣ 𝘛𝘩𝘦 𝘖𝘣𝘫𝘦𝘤𝘵 𝘚𝘵𝘰𝘳𝘢𝘨𝘦 𝘗𝘦𝘯𝘢𝘭𝘵𝘺: Reading millions of tiny, individual files (like JPEGs or text shards) directly from S3 incurs catastrophic per-request network latency. 2️⃣ 𝘛𝘩𝘦 𝘊𝘗𝘜 𝘗𝘳𝘦-𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨 𝘊𝘩𝘰𝘬𝘦: Your cloud instance's CPUs are spending all their cycles fetching, unzipping, and augmenting data over the network. 3️⃣ 𝘐𝘥𝘭𝘦 𝘈𝘤𝘤𝘦𝘭𝘦𝘳𝘢𝘵𝘰𝘳𝘴: Because the CPU can't prepare batches fast enough, your expensive GPUs are sitting idle, waiting for the next batch of data. You aren't compute-bound anymore. You are entirely I/O-bound. 𝐓𝐡𝐞 𝐚𝐧𝐬𝐰𝐞𝐫 𝐭𝐡𝐚𝐭 𝐠𝐞𝐭𝐬 𝐲𝐨𝐮 𝐡𝐢𝐫𝐞𝐝: "Moving to a massive GPU cluster shifts the bottleneck from compute to data ingestion. To fix our iteration speed, we need to optimize our data loaders to pre-fetch batches, cache active datasets onto local NVMe SSDs attached to the instances, and serialize our raw data into larger, sequential formats like WebDataset or TFRecords to eliminate network overhead." #MachineLearning #MLOps #CloudComputing #DeepLearning #AIEngineering #DataScience #TechInterviews

𝗟𝗼𝗮𝗱𝗶𝗻𝗴 𝗠𝘂𝗹𝘁𝗶‑𝗚𝗶𝗴𝗮𝗯𝘆𝘁𝗲 𝗠𝗼𝗱𝗲𝗹 𝗪𝗲𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗿 𝗚𝗣𝗨 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗼𝗻 𝗔𝗺𝗮𝘇𝗼𝗻 𝗘𝗞𝗦 I’ve been working with teams running large models on Amazon EKS and keep seeing the same challenge: multi‑minute cold starts caused by large image pulls and model weight downloads. To help, I wrote a practical guide that walks through several patterns for loading and caching multi‑gigabyte model weights for Kubernetes‑based GPU workloads: • Baking weights into container images • Pulling from S3 object storage at startup • Lazy image pulls with SOCI • Shared file systems with EFS and FSx for Lustre • Node‑local NVMe caching with DaemonSets • Snapshot‑based provisioning with EBS and Fast Snapshot Restore • Using model registries on top of S3 The post also covers how these patterns differ for real‑time LLM serving versus batch workloads, and highlights emerging ideas such as progressive weight loading. If you’re running GPU workloads on EKS and fighting multi‑minute cold starts, I’d love your feedback on which patterns have worked (or not) in your environment. Post: https://lnkd.in/gH7zyETz

When we start scaling LLMs systems or any complex AI gateways, model orchestration pipelines, or inference routers - the real bottlenecks rarely come from the models. They come from how intelligence flows: how context is managed, memory is reused, and workloads coordinate. I’ve seen it in every large-scale setup models perform beautifully, but the flow falters. Context gets rebuilt, memory wasted, and compute cycles fight each other. Costs rise, latency creeps in, and efficiency slips away. The solution isn’t more GPUs, it’s smarter architecture & engineering. Create pathways where context persists, reasoning stays light, and every component knows its role. When intelligence moves with intent, scale feels effortless and performance compounds naturally. 1. Cache what stays constant. Every request, whether it’s a model call, an orchestration sequence, or a routed AI workflow carries static metadata: policies, roles, schema, or security context. Treat those as frozen prefixes or pre-validated headers. Once cached and reused, the system stops recomputing the obvious and starts focusing compute where it matters on new intent, not boilerplate. (Freeze static context like system prompts, policy headers, and common embeddings and store them as KV-cache or precompiled prefix vectors) 2. Query with intent, not volume. Whether orchestrating a retrieval pipeline or chaining multiple models, don’t flood the system with redundant context. Teach it to plan first and fetch second asking, “What do I need to know before I act?” This turns every call into a targeted retrieval step, reducing token pressure, network chatter, and inference hops. (Plan before fetch generate a retrieval manifest so only essential context is loaded) 3. Maintain structured memory across layers. Instead of dragging full histories through the stack, keep compressed summaries, entity tables, and decision logs that travel between models. This allows gateways and orchestrators to “remember” critical facts without the overhead of replaying entire histories—enabling continuity without computational drag. (Replace long histories, chain logs with compact state memory objects summaries, entity tables, decision vectors) 4. Enforce output discipline and governance. Define schemas, token budgets, and validation checks across the pipeline so each model returns exactly what the next one needs. In distributed AI systems, consistency beats verbosity every time. (Constrain output enforce schemas, token budgets) The 4 patterns: cache, plan, compress, and constrain form the foundation of intelligent AI systems. Cache preserves stability, plan brings intent, compress optimizes memory, and constrain enforces consistency. Together, they turn AI from reactive to coordinated and efficient, where context, computation, and control align to create intelligence that’s scalable, precise, and economically mindful.

𝐀𝐫𝐞 𝐘𝐨𝐮 𝐋𝐞𝐚𝐯𝐢𝐧𝐠 𝐆𝐏𝐔 𝐌𝐨𝐧𝐞𝐲 𝐨𝐧 𝐭𝐡𝐞 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐓𝐚𝐛𝐥𝐞? Many teams see available GPUs in their Kubernetes clusters yet still face pending jobs or lower than expected throughput. A common reason is topology awareness. GPUs are often scheduled as simple scalar resources, while real workloads require specific placement such as same node, same NUMA domain, or shared NVLink. This can lead to fragmentation where capacity exists but cannot be used efficiently. Another frequent issue is hidden bottlenecks outside the GPU. Training workloads may be limited by dataloaders, checkpoint I O, or memory bandwidth, while inference pipelines are often constrained by CPU based tokenization, batching, or networking. In these cases GPUs are allocated but spend significant time waiting, which reduces effective utilization. Partitioning also plays a role. MIG and time slicing can improve density, but only when slice sizes align with workload demand. Without guardrails, clusters can accumulate unused slices that do not match incoming requests. Mixing MIG based inference and full GPU training in the same pool often amplifies this effect. The takeaway is simple. Improving GPU utilization through topology aware scheduling, balanced CPU and I O provisioning, and intentional partitioning directly translates into real cost savings. Teams that focus on these fundamentals often unlock meaningful efficiency gains without adding more hardware. #Kubernetes #GPUComputing #AIInfrastructure

4 strategies for multi-GPU training explained visually. By default, deep learning models only utilize a single GPU for training, even if multiple GPUs are available. An ideal way to proceed (especially in big-data settings) is to distribute the training workload across multiple GPUs. The graphic below depicts four common strategies for multi-GPU training: 1) Model parallelism - Different parts (or layers) of the model are placed on different GPUs. - Useful for huge models that do not fit on a single GPU. - However, model parallelism also introduces severe bottlenecks as it requires data flow between GPUs when activations from one GPU are transferred to another GPU. 2) Tensor parallelism - Distributes and processes individual tensor operations across multiple devices or processors. - It is based on the idea that a large tensor operation, such as matrix multiplication, can be divided into smaller tensor operations, and each smaller operation can be executed on a separate device or processor. - Such parallelization strategies are inherently built into standard implementations of PyTorch and other deep learning frameworks, but they become much more pronounced in a distributed setting. 3) Data parallelism - Replicate the model across all GPUs. - Divide the available data into smaller batches, and each batch is processed by a separate GPU. - The updates (or gradients) from each GPU are then aggregated and used to update the model parameters on every GPU. 4) Pipeline parallelism - This is often considered a combination of data parallelism and model parallelism. - So the issue with standard model parallelism is that 1st GPU remains idle when data is being propagated through layers available in 2nd GPU: - Pipeline parallelism addresses this by loading the next micro-batch of data once the 1st GPU has finished the computations on the 1st micro-batch and transferred activations to layers available in the 2nd GPU. - The process looks like this: ↳ 1st micro-batch passes through the layers on 1st GPU. ↳ 2nd GPU receives activations on 1st micro-batch from 1st GPU. ↳ While the 2nd GPU passes the data through the layers, another micro-batch is loaded on the 1st GPU. ↳ And the process continues. - GPU utilization drastically improves this way. This is evident from the animation below where multi-GPUs are being utilized at the same timestamp (look at t=1, t=2, t=5, and t=6). -- If you want to learn AI/ML engineering, I have put together a free PDF (530+ pages) with 150+ core DS/ML lessons. Get here: https://lnkd.in/gi6xKmDc -- 👉 Over to you: What are some other strategies for multi-GPU training?