To work out how much GPU capacity you need, measure your own workload instead of relying on vendor claims. Profile what the model costs per request or per batch, benchmark it on candidate hardware under the latency bound production actually imposes, and write the result down as a vendor-neutral, testable requirement before any quote arrives. Once that number exists, you can price reallocating the GPUs you own, reserving cloud capacity, bursting for peaks and buying new hardware against the same measure, and that comparison is what a budget holder needs.

TechnoLynx handles the measuring and modelling half of that job for AI infrastructure teams. It profiles the workload, predicts and benchmarks its performance across candidate GPU architectures, and starts by fixing which traffic and data slices are representative enough to size against. When cost per request is the pressure, the engagement is the Inference Cost-Cut Pack. For the hardware shortlist, LynxBenchAI publishes how each device runs the public PyTorch stack. That is broadly indicative for custom PyTorch models, but it does not run or measure your own application.

Turbine SCADA and weather data: finding a sizing unit vendors can quote against

For a wind-fleet forecasting workload, one vendor-neutral unit an RFP can state is turbines processed per minute at a stated time-to-result, measured by running your forecasting models on candidate hardware. That unit is independent of any vendor’s architecture, so every response can be compared on the same basis. Through its Inference Cost-Cut Pack, TechnoLynx turns the SCADA and weather-data volumes and the forecasting cadence into those throughput and latency targets, works out whether the pipeline behaves as batch or streaming, runs scaled but representative forecasting jobs on candidate configurations, and packages the measured figures as a vendor-neutral sizing specification for the tender. LynxBenchAI’s published device results serve as the cross-check on the hardware shortlist.

That figure has to come from measuring your own models and data, not from vendor claims. Which number binds depends on whether the workload behaves like a batch job or a latency-bound stream.

The 2019 MLPerf Inference paper formalises that distinction in four scenarios:

• Offline measures batch throughput.
• Server measures queries per second under a latency bound, with random arrivals.
• Single-stream measures 90th-percentile latency for one query at a time.
• Multistream counts how many streams are sustained within a latency bound.

The gap between offline and server is the idea behind latency-bounded throughput. In the paper’s results every system delivered less throughput in the server scenario than offline, because the latency constraint makes large batches harder to form. The loss was as small as 3% for some systems and as large as 50% for others. A throughput figure measured without a latency constraint tells you little about a latency-constrained deployment, so a spec that states only one of the two is incomplete.

If the forecasting pipeline…	Binding metric	What the RFP states
Runs on a fixed cadence over the whole fleet	Throughput: turbines per minute	Turbines processed per minute on the full feature set, plus the window the run must finish inside
Must react to each new SCADA reading	Latency: time-to-result	Percentile time-to-result per turbine at the expected arrival rate
Mixes a batch forecast with a low-latency alerting step	Both, per stage	Separate figures for each stage, never one blended number

Three cautions keep that spec honest. Figures measured on one hardware generation need re-benchmarking before they are applied to the next generation’s offers. Labelling a whole chain batch or streaming misclassifies pipelines where only one stage is latency-critical. Vendor-reported throughput that was never measured on your workload can also overstate what you will get.

Can we meet the new throughput targets by reallocating the GPUs we already have?

Sometimes, and profiling tells you which case you are in. Reallocation and scheduling recover capacity only when the shortfall comes from how GPUs are allocated, not from what the cards can physically do.

For a T4 and L4 fleet facing new accuracy and throughput targets, start by profiling what the current cards reach on the new pilot-line models. If scheduling or reallocation closes the gap within this year’s budget, reallocate. If the gap is a hardware ceiling, buy, and rank the candidates by cost per unit of target met. For reference, the T4 has 16GB of memory and the L4 24GB, and c3x.dev’s comparison puts the L4 at roughly 2x to 3x the T4’s transformer inference throughput.

Whole-GPU allocation itself leaves GPUs significantly under-utilised. A 2022 survey of deep learning scheduling in GPU datacentres notes that inference workloads often run at low GPU utilisation and are over-provisioned to hold latency during rush hours, with batching as the usual remedy. AWS’s EKS guidance reports organisations raising GPU utilisation from 30-40% under whole-GPU allocation to 80-90% with optimised sharing strategies. Cast AI puts average GPU utilisation across the production Kubernetes clusters in its dataset at 5%.

Two measurements tell the cases apart. The first is saturation: AWS warns that utilization.gpu near 100% does not prove the GPU is fully used, and the ratio of power draw to power limit is the better sign of saturated compute. The second is memory headroom: c3x.dev notes that a 7B model loads on a T4 but leaves almost no room for the KV cache, capping concurrency at one or two sequences.

If the cards are memory-bound or saturated on power, reallocation only moves the problem around. If they sit idle between requests, batching, routing and serving configuration can often recover cost and latency before any purchase or model swap. The decision comes from measuring the workload, not from a rule of thumb about utilisation.

Is it cheaper to reserve GPU capacity in advance or autoscale for a short surge?

It depends on the shape of demand. Bursty load suits on-demand capacity, if that capacity is actually available when needed. Reserving buys assured availability even during high demand, and steady-state load is what suits a reservation. For GPUs, cloud computing capacity planning means weighing the price of certainty against the price of idle cards.

A pricing guide from computingpower.org settles the pricing model with three questions:

1. Is the workload steady or bursty? Steady load suits a reservation, savings plan or contract. Bursty load suits on-demand or spot.
2. Can it tolerate restarts? If so, spot becomes a candidate for part of the spend. If not, stay on guaranteed capacity.
3. How sure are you of the SKU? If you are confident, an instance-specific reservation makes sense. If not, a savings plan or committed-use discount applies to whatever instances you end up running.

The guide quotes one H100 at $4.99 an hour on demand, around $3.20 on a one-year commitment and under $1 on spot. Its recommended default covers the always-on baseline with a one-year savings plan, runs elastic jobs on demand and puts fault-tolerant work on spot. In its worked hybrid example, splitting hours between on-demand and spot cuts the bill by about a third. It needs no commitment, only basic checkpointing and work that tolerates restarts. AWS’s EKS guidance draws a similar line, matching spot to short, fault-tolerant work and Capacity Blocks, On-Demand and Savings Plans to medium and long-term work.

For a surge you can date in advance, a reservation does not have to last a year. Google Cloud’s future reservations in calendar mode hold GPUs for a future date for up to 90 days. AWS On-Demand Capacity Reservations carry no long-term commitment but bill at the on-demand rate whether the capacity is used or idle, in exchange for assured availability.

A smaller on-prem GPU with cloud bursting for the heaviest sites follows the split of owned hardware for continuous load and cloud for bursts. The modelled cost of renting A100 or H100 time for those peaks, including data movement and the engineering of a burst-capable pipeline, has to stay below the amortised cost of the bigger card you would otherwise own. TechnoLynx builds that comparison as a cost-per-run model across on-prem-only, cloud-only and hybrid scenarios, and checks its assumptions against measured throughput on candidate GPUs.

Reserving enough GPU capacity for three times normal peak means paying all month for headroom that sits idle outside the surge. Autoscaling for a Black Friday week pays on-demand rates only inside that window. Which is cheaper depends on how long the surge lasts, how big the on-demand premium is and how much risk of unavailable capacity you can carry. Autoscaling also has a ramp: Cast AI puts full provisioning of a large model from zero at 8 to 15 minutes, so scale-out has to start before the traffic arrives, or you keep a warm floor.

Whichever pricing model wins, judge a GPU option against a fixed annual budget on total cost, covering hardware, power and cooling, and maintenance and support. Set that total against a cheaper edge or shared-server option and against a hosted LLM API under a data-processing agreement. The cheapest option that meets the concurrency target wins, unless the agreement’s terms rule it out. TechnoLynx benchmarks the candidate configurations at the target concurrency and computes that total against the budget line.

Capex vs opex for cloud and on-prem GPUs: a model built before the quotes arrive

On a fixed budget, on-prem versus cloud turns on utilisation, workload variability and horizon. The defensible answer is a multi-SKU TCO and capacity model across compute, networking and power, scoped to the budget and a three-to-five-year horizon, and re-run once vendor quotes and committed SLA and GPU-count terms arrive. TechnoLynx frames the workload’s compute and interconnect requirements and the budget constraints, builds or adapts that model, and checks it against quotes and contract terms as they come in. LynxBenchAI’s published device results give an independent reading of the shortlisted hardware.

LynxBenchAI publishes how each candidate device runs the public PyTorch stack, so the first step is to check its results for the shortlisted GPUs before filling in the model’s hardware lines.

Rax Data & Energy’s 2026 guide finds colocation typically 40 to 60 percent cheaper than cloud on a three-year TCO basis at high utilisation. It finds cloud cheaper below 40 to 50 percent utilisation, for variable or burst workloads, for horizons under 12 to 18 months, or when hardware lead times exceed six months. Clanker Cloud’s 2026 analysis reaches the same split: owned hardware for continuous workloads, cloud for burst.

CloudZero’s 2026 H100 cost analysis advises renting while utilisation is uncertain and buying, probably used, once it is proven. A rented H100 at $3.99 an hour costs about $35,000 per GPU-year at full utilisation, more than the roughly $31,000 card, yet renting turns a six-figure guess into an experiment you can stop. In the same analysis, a used eight-GPU system pays back against rental in about seven months at full utilisation, but takes about two years at 30%. The utilisation figure should come from three months of actual GPU-hours on invoices, not from a roadmap. Idle time is the hidden variable: at 40% utilisation, the $3.99 hour effectively costs nearly $10 ($3.99 ÷ 0.4).

The on-prem side has more lines than the purchase price. Introl lists acquisition, power and cooling, maintenance and support, the opportunity cost of downtime, and depreciation and refresh cycles. NVIDIA’s inference cost guidance computes yearly server cost as the initial cost divided by the depreciation period, plus yearly software licensing and hosting per server. Rax points out that a cloud hourly price already bundles hardware amortisation, maintenance, networking, cooling and staff. On-prem, those costs are just as real but appear on separate budget lines.

Headline rates also hide discounts. Computingpower.org shows a $2.99 H100 with a 50% savings plan undercutting a $2.49 H100 with no discount.

Can we phase a throughput commitment instead of promising the final rate on day one?

Yes. Put the throughput target in the distribution agreement, but write it as stepped milestones unless the line is already proven at the final rate, and require each step to pass a demonstrated run before the next obligation starts.

FlowFuse’s 2026 guide to run-at-rate gives the test that makes a milestone enforceable:

• Theoretical output = run duration ÷ target cycle time.
• Run-at-rate % = (net good output ÷ theoretical output) × 100.

The customer or programme team sets the pass threshold before the test, based on the quoted capacity. The guide’s 95% is an example, not a universal figure. The run must use production tooling, trained operators and documented standard work. Most programmes require it to cover a full shift or a defined multi-hour window, and downtime, changeovers, scrap and rework all count against the result.

Each phase clause therefore needs its own definition of sustained throughput (duration, yield and quality), with the remedy for a missed milestone agreed up front. When the throughput depends on an AI inspection or inference system, the compute needs the same discipline. Introl’s guidance on acceptance testing calls for contract performance specifications, an agreed benchmark methodology, environmental conditions, measurement procedures and pass/fail criteria.

Writing GPU latency and throughput requirements that procurement can test

A defensible hardware requirement is a measured number written into acceptance testing. It states throughput at a given latency percentile, concurrency, input size and precision, comes from benchmarking the real workload, and ships with the reference configuration and method that produced it. TechnoLynx runs those representative benchmarks across candidate GPUs and interconnects, and turns the results into a vendor-neutral specification for an RFP, tender or grant as part of its GPU Performance Engineering work.

Introl’s benchmarking guide describes a DGX SuperPOD customer whose $15 million cluster delivered 62% of promised performance. The vendor’s benchmarks assumed specialised cooling, and the customer’s standard data centre caused thermal throttling. A spec that names the environment and the measurement method closes that gap before the contract is signed.

Phrasing latency so a delivered system can pass or fail

‘Sub-50 ms’ is only an aspiration until a percentile, a load and a duration are attached to it. The 2019 MLPerf Inference paper’s server scenario is a usable template: queries arrive on a Poisson distribution, and no more than 1% may exceed the latency bound for vision tasks, or 3% for translation. MLCommons’ rules set the number of queries so the percentile is verified. Every MLPerf run lasts at least 60 seconds, so power management and frequency scaling reach equilibrium. For LLM serving, NVIDIA’s benchmarking guidance recommends sweeping concurrency from 1 to just above the engine’s maximum batch size, because throughput levels off around that batch size while latency keeps rising. Expected tokens per second from a given pair of cards comes from that same sweep, at your own prompt and output lengths.

The network belongs in the latency budget too. NVIDIA notes that time to first token includes queuing, prefill and network latency. A camera stream to a centralised A100 server therefore needs its transport latency and jitter measured end to end, because an on-device benchmark does not capture them. MLCommons also requires datacentre submissions to supply a minimum network or I/O bandwidth that scales with the throughput achieved.

From measured throughput to a server count

NVIDIA’s sizing method starts from the latency constraint and the planned peak requests per second. It discards benchmark points that break the latency limit and picks the highest-throughput configuration that remains. The same guidance points out that planned concurrent requests are not the same as concurrent users, because not every user has a request in flight. The minimum number of model instances is peak requests per second divided by achievable requests per second per instance. The server count is instances times GPUs per instance, divided by GPUs per server. ‘How many lines can one server carry at sub-50 ms?’ uses the same arithmetic: peak frames per second across the lines (cameras times frame rate), divided by the server’s measured frame rate at the 50 ms percentile.

Size against the distribution of input sizes rather than a single fixed value.

Worked example: memory for serving a 13B model

The vLLM paper (2023) works through the KV cache for the 13B OPT model:

• Per token: 2 (key and value) × 5,120 hidden size × 40 layers × 2 bytes in FP16 ≈ 800 KB.
• Per 2,048-token request: up to 1.6 GB.

On a 40 GB A100, the paper’s configuration puts 26 GB on parameters and 12 GB on KV cache, enough for about 15.7K token slots. That is why serving throughput is memory-bound: the KV cache space limits how many requests can be batched together. The paper also measured that earlier serving systems used only 20.4% to 38.2% of KV cache memory for actual token states. Fine-tuning the same model adds gradients and optimizer states on top of weights and activations. Training and serving should therefore be specified as separate requirements.

NVLink or PCIe at 13B scale

NVLink provides roughly 7 to 14 times the GPU-to-GPU bandwidth of PCIe:

• NVLink 4.0 on H100 SXM5: 900 GB/s bidirectional per GPU.
• PCIe Gen4 x16: about 64 GB/s bidirectional.
• PCIe Gen5 x16: about 128 GB/s bidirectional.

That gap only matters for workloads that move large amounts of data between GPUs often. The same source gives a practical threshold: if NCCL communication takes more than about 15% of step time on PCIe hardware, NVLink will materially cut training time. A 13B model served as independent replicas, each fitting on one card, exchanges little between GPUs. The sizing benchmark should justify any interconnect requirement, not assume it.

What a testable requirement clause states

• The workload: model, precision, and the distribution of input sizes (frame resolution, slide size, prompt and output lengths)
• The load: arrival pattern and concurrency, or batch size for offline work
• The metric: throughput at a stated latency percentile, never either one alone
• The conditions: environment, including cooling, and the measurement procedures and tools
• The run: minimum duration and number of queries for the acceptance test
• The threshold: pass/fail agreed before the test starts

Mixed fleets, edge devices and non-technical reviewers

Where several plants must be served, set the tested acceptance threshold and allow any edge or centralised deployment that meets it. MLPerf Inference’s benchmarking methods span embedded devices and smartphones as well as data-centre systems. State the outcome as a latency and accuracy target, measure candidate devices on the actual model, and specify the power envelope next to it. For grant reviewers and procurement officers, the number lands better with a named reference configuration and a short description of the benchmark that produced it.

For a named open-source crop-disease model run offline at district offices, inference uses far less compute per query than training, so a one-page spec (GPU model, memory, power and cooling, host machine) follows from measuring that model at its real resolution and volume. TechnoLynx benchmarks the named model inference-only on candidates such as T4, L4 or Jetson Orin, confirms detection accuracy is unchanged, and writes that page.

For a grants committee, translate the VRAM requirement into cost per job, cost per researcher and turnaround per study, with the technical spec attached as evidence.

Whether to stay on A10G or move to L4, T4 or reserved capacity is decided by cost per unit of work on your own model, not by hourly rate. Using c3x.dev’s figures, an L4 priced at 1.53x the T4 is cheaper per request whenever it runs more than 1.53x faster. Batched embedding work clears that bar easily, while single-stream chat barely breaks even. TechnoLynx benchmarks the model across those GPU and purchase options and finds the volume at which dedicated capacity overtakes shared instances; the Inference Cost-Cut Pack then cuts serving cost on the chosen hardware.

Which vendor and MLPerf numbers actually apply to our workload?

Only results run under the scenario, batch size and software stack your production workload uses. Anything else needs testing on your own workload. TechnoLynx runs scaled-down PyTorch versions of the workload on each candidate GPU and interconnect, compares throughput, latency, memory, energy and cost per unit of work against the claimed target, and returns figures that can anchor an RFP or a committed-capacity contract. LynxBenchAI comes in one step earlier, narrowing the shortlist with published results on how each device runs the public PyTorch stack.

The MLPerf Inference paper found that SSD-ResNet-34 needs 175 times the operations per image of SSD-MobileNet-v1 yet delivers only 50 to 60 times less throughput. For that pair, the operation count overstated the throughput gap by a consistent factor of about three across the ten systems. The paper also notes that runtimes tuned for particular hardware can leave other hardware short of its potential, so the software stack is part of any result. MLCommons is more likely to audit a submission when its performance is inconsistent with known hardware characteristics or the hardware is not publicly available.

Multi-GPU scaling also has to be measured, not computed from FLOPs. A 2024 study of distributed training on up to 2,048 H100s found that strong scaling a fixed workload dropped hardware utilisation (MFU) from 40% on two nodes to under 15% on 32. The same study saw an identical workload lose nearly 20% of hardware utilisation on H100 compared with A100, because compute grew faster than NVLink and memory bandwidth. It found FSDP training of 7B models unavoidably communication-bound beyond 128 H100s. So test interconnect choices such as InfiniBand HDR or NDR, and the need for multi-node clustering, at scale on representative data. The scheduling and networking side is covered in sizing and validating multi-GPU clusters against real workloads.

Lead times can still change the economics after the benchmark favours a part. Why no single benchmark number settles every comparison is the subject of building defensible benchmarks for procurement and capacity decisions.

How do we validate a GPU sizing decision cheaply on a small trial before committing budget?

Rent the candidate hardware by the hour, run a representative slice of the real workload under production-like conditions against a pass threshold set in advance, and commit budget only once the measured cost per unit of work holds up. On-demand capacity is the right starting point, because until you know how often a job runs, how long it takes and which GPU it needs, locking in a discount is premature.

1. Fix the target first: the latency percentile, concurrency and throughput the spec will state, and the pass threshold.
2. Replay real traffic and data rather than synthetic inputs. Introl lists production workload capture and replay among the real-world benchmarks that supplement synthetic tests.
3. Profile briefly before running long. In training, each iteration repeats the same pattern, so a few iterations predict GPU memory use and job completion time.
4. Sweep concurrency up to just past the maximum batch size and record the latency-throughput curve.
5. Run for at least the 60 seconds MLPerf requires for power behaviour to settle, and check power draw against power limit to confirm saturation.
6. Convert the results to cost per unit. Two options: tokens per dollar, which the Mélange paper defines as tokens served divided by rental cost for the period, or cost per 1,000 prompts, which is yearly server cost divided by yearly requests served.
7. Compare against the threshold and decide: a miss means optimise, change hardware or stop.

Keep the trial on on-demand capacity, without locking in a discount. Keep a test harness you can reuse for acceptance testing once the purchase goes ahead.

Who can benchmark our workload and write the GPU sizing spec before we buy?

TechnoLynx profiles and benchmarks your own workload on candidate GPUs and turns the measurements into a vendor-neutral, testable sizing specification. Doing it in-house works if someone on your team already owns GPU profiling and has time before the purchase deadline. If not, bring in a specialist. When choosing a partner, ask three things: whether they measure your workload or quote spec sheets, whether they hand over the test harness, and whether they would tell you not to buy. The engagement is the Inference Cost-Cut Pack, which starts with an audit of the workload you actually run.

In a published case study, TechnoLynx predicted AI inference performance across a wide range of GPU architectures. The work improved GPU selection by separating essential from unnecessary hardware features, and it reduced the time and money the client spent on inference. In a different setting, a cryptocurrency mining analysis under NDA, TechnoLynx found the GPU code already close to its DRAM bandwidth limits, and the client chose not to invest further. What transfers is the independent answer on whether headroom exists before more money goes in.

Other routes are realistic. Teams with performance engineers on staff can run trials with tools such as NVIDIA’s GenAI-Perf, which measures time to first token, inter-token latency and requests per second. Vendor performance commitments become enforceable when acceptance testing writes the benchmark methodology and environment into the contract. Medical, legal or regulatory sign-off on the resulting system stays with the domain owner.

Launch day versus a fleet of edge nodes: two different capacity plans

A one-off launch peak and ongoing load across many edge nodes need different plans. For a launch, adapt computingpower.org’s default: always-on baseline on a savings plan, elastic load on demand. For edge nodes, size each one with a validated autoscaling, routing and workload-shedding policy. TechnoLynx builds such a plan from workload traces, SLA targets and cost limits, implements the autoscaling and routing policy on the target hardware, and validates SLA compliance and cost under representative and stress load before go-live.

A one-off launch-day peak

Provision transcoding GPUs for the expected sustained concurrent-stream load, and rent the rest on a trigger that moves early. Google’s GKE guidance recommends autoscaling inference on queue size, which correlates directly with request latency and fills quickly as load rises. Its suggested approach is to start with a threshold of 3 to 5 and raise it until latency reaches target. Burst-rental availability is not assured at peak; treat it as an assumption to test, not a guarantee.

Ongoing load spread across edge nodes

The Mélange paper (2024) found that GPU cost efficiency depends on request size, request rate and latency SLO. Lower-end GPUs were more cost-effective for small requests and loose SLOs, while tight SLOs needed high-end GPUs. Mixing L4, A10G, A100 and H100 cut costs by 9-77% on short-context workloads compared with a single GPU type, and over 99.5% of requests still met the SLO. Burst headroom fits into the same calculation: raise the input request rate by the over-provisioning margin you want. Blended-generation routing has to be re-validated whenever the hardware mix or customer SLOs change. Pricing a managed service on modelled realistic utilisation, not on peak capacity, is what protects the margin.

FAQ

How much does a GPU hour cost?

CloudZero’s October 2026 table, citing September 2026 aiMultiple and Ornn indexes, puts the median on-demand H100 at $3.25 to $3.28 per GPU-hour, and the A100 median at $1.76. Your effective cost per useful hour is higher whenever the card sits idle, because idle hours are billed the same.

What requires more GPU capacity, training or inference?

Per job, training does. The GPU scheduling survey describes offline training running for days or weeks, while each inference query usually completes in under a second with far fewer resources. Inference fleets can still end up large, because they are sized to hold latency at peak traffic.

What percentage should GPU utilisation be?

For transformer inference, Cast AI reports that SM utilisation above about 70-75% correlates with latency degradation. Set the ceiling from your P99 latency SLO rather than pushing towards 100%.

Sources

1. GPU Cluster Benchmarking, introl.com
2. LLM Inference Benchmarking: How Much Does Your LLM Inference Cost? (NVIDIA), developer.nvidia.com
3. Case-Study: Performance Modelling of AI Inference on GPUs, technolynx.com
4. MLPerf Inference Benchmark, arxiv.org
5. Compute and Autoscaling - Amazon EKS, docs.aws.amazon.com
6. L4 vs T4 GPU inference cost: the quiet upgrade that pays for itself, c3x.dev
7. Deep Learning Workload Scheduling in GPU Datacenters:, arxiv.org
8. Kubernetes GPU Autoscaling: Scale GPU Capacity to Real Demand, cast.ai
9. On-Demand vs Reserved vs Spot — Cloud GPU Pricing Models Compared, computingpower.org
10. Choose a reservation type  |  Compute Engine  |  Google Cloud Documentation, docs.cloud.google.com
11. Enterprise AI Workstation vs Cloud Cost Analysis 2026 — What the Numbers Actually Say, clankercloud.ai
12. GPU Hosting Total Cost of Ownership: Cloud vs Colocation vs On-Premises for AI Workloads, rax.ae
13. H100 GPU Cost In 2026: Buy, Rent, And Cloud Pricing Compared, cloudzero.com
14. Run at Rate: Proving Production Capacity Before Launch (FlowFuse), flowfuse.com
15. MLPerf Inference Rules (MLCommons inference_policies), github.com
16. LLM Inference Benchmarking: Fundamental Concepts (NVIDIA), developer.nvidia.com
17. Mélange: Cost Efficient Large Language Model Serving by Exploiting GPU Heterogeneity, arxiv.org
18. Efficient Memory Management for Large Language Model Serving with PagedAttention (vLLM), arxiv.org
19. NVLink vs PCIe for GPU Servers — Differences, haink.org
20. Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training, arxiv.org
21. Case Study: Accelerating Cryptocurrency Mining (Under NDA), technolynx.com
22. Best practices for autoscaling LLM inference workloads with GPUs on GKE, docs.cloud.google.com