{"post":{"slug":"gpu-procurement-guide-2026","title":"GPU Procurement: Buying Compute Without Overbuying","excerpt":"Memory capacity usually decides which GPU you need, not raw compute. Getting that backwards is how teams end up with fast hardware that cannot hold their model.","category":"ai","date":"January 10, 2026","author":"ByteLedger Team","content":"<span class=\"dropcap\">T</span>he specification sheet leads with compute throughput, so that is what gets compared. For inference workloads it is frequently the least relevant number on the page.\n\nWhat decides whether a GPU works for you is whether your model and its cache fit in memory, and how fast that memory can be read. Compute matters, and it is rarely the thing you run out of first.\n\n## What changed in 2026\n\n- **Memory capacity became the headline selection criterion.** As models and contexts grew, fitting the workload displaced raw speed as the primary question.\n- **The rental market matured.** Committed-capacity and spot options made owning hardware a narrower proposition — see [reserved GPU capacity](/blog/reserved-gpu-capacity-2026).\n- **Interconnect specifications got scrutinised.** Teams learned that multi-GPU strategy depends on link bandwidth, not GPU count.\n- **Older generations stayed viable.** For inference at moderate scale, previous-generation hardware with adequate memory remained cost-effective.\n\n## What to compare\n\n| Specification | Matters for | Priority for inference |\n| --- | --- | --- |\n| Memory capacity | Whether the workload fits | Highest |\n| Memory bandwidth | Generation speed | High |\n| Interconnect bandwidth | Multi-GPU strategy | High if scaling out |\n| Compute throughput | Prefill speed, training | Moderate |\n| Power and cooling | Operating cost, density | Moderate |\n\n**Memory capacity first.** Model weights plus KV cache for your target concurrency plus overhead must fit. Under-provision and you either cannot run the model or run it at concurrency too low to be economical. This is a hard constraint, not a performance gradient.\n\n**Bandwidth second.** Token generation reads the weights repeatedly, so bandwidth sets the ceiling on generation speed more directly than compute does — see [GPU utilisation](/blog/gpu-utilization-explained-2026).\n\n**Interconnect third, and it is a gate rather than a gradient.** Fast intra-node links make [tensor parallelism](/blog/tensor-parallelism-explained-2026) viable; slow ones do not. If you will split a model across devices, this determines which strategies are available at all.\n\n**Compute last for inference.** It matters for prefill and for training, and it is rarely the first thing you exhaust when serving.\n\n## Sizing the memory requirement\n\nWork it out rather than guessing, because the cache term surprises people.\n\nStart with model weights at your serving precision. Quantisation reduces this substantially and is frequently the cheapest way to fit a model on smaller hardware — see [quantization explained](/blog/quantization-explained-2026).\n\nThen add KV cache, which scales with context length times concurrent sequences. This is the term that dominates at long contexts, and it is where teams under-provision because they sized for the weights alone.\n\nThen leave headroom for activations and fragmentation.\n\nThe useful exercise is computing the concurrency your memory supports at your typical context length, then checking whether that concurrency meets your throughput requirement. If it does not, you need more memory rather than more compute — a conclusion that is only visible if you do the arithmetic.\n\n## Rent before you buy\n\nOwned hardware is cheaper per hour at high utilisation and worse in every other respect: capital outlay, a fixed capacity that cannot follow demand, and an obsolescence curve that has been steep.\n\nThe pattern that works: rent while your workload is uncertain, measure actual sustained utilisation over months, and consider owning only the stable baseline. Utilisation on owned hardware almost always comes in below forecast, because forecasts are built from peaks.\n\nWhere you do commit, committing to a baseline and renting the peaks beats owning for peak. Idle owned capacity is pure loss; overflow rental is a variable cost you only pay when you need it.\n\n## Common mistakes\n\n- **Comparing on compute throughput.** Rarely the binding constraint for inference.\n- **Sizing memory for weights only.** The cache term dominates at long contexts.\n- **Ignoring interconnect.** It determines which parallelism strategies work.\n- **Buying for peak demand.** Idle capacity is unrecoverable.\n- **Assuming newest is required.** Previous generations with adequate memory are often better value.\n- **Not measuring utilisation before buying.** Forecasts overstate it consistently.\n- **Overlooking power and cooling.** Real constraints in a rack, and real cost.\n\n## FAQ\n\n**How much memory do I need?**\nCompute it: weights at serving precision, plus cache for your concurrency at your context length, plus headroom. There is no rule of thumb because the cache term depends entirely on your workload shape.\n\n**Is it cheaper to own?**\nOnly at high sustained utilisation, and only if you measured that utilisation rather than forecasting it. Most teams over-estimate.\n\n**Should I buy consumer or datacentre hardware?**\nConsumer cards can be cost-effective for development and small-scale inference. Datacentre parts bring more memory, better interconnect, and licensing terms that matter for production deployment.\n\n**Does quantisation change what I should buy?**\nSubstantially — it can move a workload from requiring several GPUs to one. Establish your serving precision before selecting hardware, not after.\n\n## Where to go next\n\nFor the throughput metrics that should guide selection, read [GPU utilisation](/blog/gpu-utilization-explained-2026). For committed capacity options, [reserved GPU capacity](/blog/reserved-gpu-capacity-2026) and [spot instances for training](/blog/spot-instances-for-training-2026).","hue":196,"metaTitle":"GPU Procurement: Buying Compute Without Overbuying","metaDescription":"How to choose GPUs for AI workloads — why memory capacity and bandwidth matter more than FLOPs, and when renting beats owning.","keywords":["gpu procurement","choosing gpus for ai","vram requirements llm","memory bandwidth gpu","buy vs rent gpu","ai hardware planning","interconnect nvlink","ai infrastructure 2026"],"tldr":["**Memory capacity is usually the binding constraint** — a model that does not fit cannot run, however fast the device is.","**Memory bandwidth matters more than FLOPs for inference** — generation is bandwidth-bound.","**Interconnect determines your parallelism options** — slow links rule out tensor parallelism.","**Rent before you buy** — utilisation on owned hardware is almost always lower than forecast.","**Skip:** buying for peak demand. Own the baseline, rent the peaks."],"readTime":"4 min read","wordCount":832},"related":[{"slug":"gpu-memory-estimate-llm-2026","title":"GPU Memory for LLMs: Estimating What You Actually Need","excerpt":"Model size is only the first term. Here is how to estimate memory for inference and training, including the parts that catch people out at deployment.","category":"ai","date":"July 24, 2026","author":"ByteLedger Team","readTime":"3 min read","hue":240},{"slug":"ai-chips-explained-2026","title":"AI Chips Explained: What Accelerators Actually Do in 2026","excerpt":"AI chips are not just faster processors, they are built around a different kind of math entirely. Here is what makes a GPU, TPU, or NPU good at running neural networks and why memory matters as much as speed.","category":"ai","date":"July 27, 2026","author":"ByteLedger Team","readTime":"4 min read","hue":221},{"slug":"gpu-utilization-explained-2026","title":"GPU Utilisation: The Metric That Lies to You","excerpt":"Your dashboard shows 95% GPU utilisation and throughput is poor. The metric counts whether the device is busy, not whether it is doing anything worthwhile.","category":"ai","date":"January 26, 2026","author":"ByteLedger Team","readTime":"4 min read","hue":218}],"sameCategory":[{"slug":"agent-cost-attribution-2026","title":"Agent Cost Attribution: Finding Where the Tokens Went","excerpt":"A single monthly API bill tells you that agents are expensive. It does not tell you which feature, which customer, or which retry loop is responsible. That gap is fixable.","category":"ai","date":"August 19, 2026","author":"ByteLedger Team","readTime":"5 min read","hue":220},{"slug":"human-in-the-loop-agents-2026","title":"Human-in-the-Loop Agents: Where to Put the Approval Gate","excerpt":"Approve everything and the agent is a slow autocomplete. Approve nothing and you have handed irreversible actions to a system that can be talked into things.","category":"ai","date":"August 19, 2026","author":"ByteLedger Team","readTime":"5 min read","hue":197},{"slug":"reasoning-effort-controls-2026","title":"Reasoning Effort Controls: Tuning How Hard a Model Thinks","excerpt":"Fixed thinking budgets gave way to effort levels, and the default is rarely the right setting. Here is how the dial behaves and where each level actually belongs.","category":"ai","date":"August 19, 2026","author":"ByteLedger Team","readTime":"5 min read","hue":210},{"slug":"retrieval-metrics-explained-2026","title":"Retrieval Metrics: Measuring the Half of RAG That Fails","excerpt":"When a RAG system answers badly, the retrieval usually failed before the model ever spoke. Measuring generation quality alone hides that entirely.","category":"ai","date":"August 19, 2026","author":"ByteLedger Team","readTime":"5 min read","hue":215}],"newer":{"slug":"go-bag-checklist-2026","title":"The Go Bag: What You Would Grab With Ten Minutes","excerpt":"If you had to leave the house quickly, what would you need? Assembling that once, in a bag by the door, converts a frantic ten minutes into picking something up.","category":"guides","date":"January 10, 2026","author":"ByteLedger Team","readTime":"4 min read","hue":171},"older":{"slug":"hmac-request-signing-2026","title":"HMAC Request Signing: Proving a Request Is Genuine","excerpt":"A shared secret in a header proves the sender knows the secret. Signing the request proves the request itself was not altered, which is a considerably stronger claim.","category":"code","date":"January 10, 2026","author":"ByteLedger Team","readTime":"4 min read","hue":310}}