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Estimate an AI inference server in two separate dimensions: capacity (whether weights, KV cache, activations, and runtime allocations fit) and bandwidth (how quickly the system can move data during execution). Start with the model, precision, context distribution, concurrency, and service targets; calculate weight and KV-cache memory; reserve 10–15% planning overhead; then validate the result with the target runtime on the intended GPU topology. A GPU’s advertised VRAM or peak bandwidth by itself cannot guarantee a tokens-per-second or latency target.
Capacity and bandwidth answer different questions
Memory capacity is a fit constraint. The server must hold the model weights, resident key-value (KV) cache, intermediate activation tensors, input/output tensors, framework allocations, communication buffers, and safety headroom. If the combined requirement exceeds usable VRAM, requests will fail, be evicted, or spill into slower memory tiers.
Memory bandwidth is a data-movement constraint. It describes how quickly a GPU can read and write data to its high-bandwidth memory under ideal hardware conditions. Real throughput also depends on the model architecture, prompt and output lengths, precision, batching, cache behavior, runtime, kernel implementation, GPU-to-GPU links, and scheduling. Treat a bandwidth specification as an input to an estimate, not as a performance result.
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NVIDIA’s Dynamo deployment guide states the fit condition this way: “The only hard requirement for a model to fit is that the combined memory of the pooled GPUs clears the model, with headroom for the KV cache and framework overhead.” In practice, that condition is necessary but not sufficient for a service-level objective: a model can fit while still missing latency or throughput targets.
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Collect workload inputs before choosing a GPU
Write down the workload you actually intend to serve. A calculation based only on a model’s parameter count can be wrong by tens of gigabytes when context length or concurrency changes.
- Model and architecture: identify the exact checkpoint, parameter count, number of transformer layers, attention-head layout, head dimension, and whether the model uses grouped-query or another reduced-KV attention scheme.
- Weight precision: record the checkpoint and serving format separately. BF16, FP16, FP8, INT8, FP4, and compressed formats do not consume the same amount of memory, and quantization metadata adds overhead.
- KV-cache precision: the cache may use a different data type from the weights. Record the bytes per cache element used by the serving engine.
- Prompt and output distributions: capture typical and maximum input tokens and generated tokens. A maximum-context setting is not the same as the average resident context.
- Concurrency: specify simultaneous sequences, batch size, and whether requests share or retain prefixes.
- Service objectives: define time to first token, inter-token latency, end-to-end request latency, output-token throughput, queueing limits, and error-rate targets.
- Runtime and version: record the serving backend, version, attention implementation, quantization implementation, and relevant cache settings.
- Hardware topology: list GPU model and count, usable VRAM, GPU-to-GPU interconnect, host-memory path, and whether all GPUs are in one node.
NVIDIA’s reference architecture recommends recording GPU type, backend, software versions, concurrency, prompt and output distributions, time to first token, inter-token latency, request latency, output throughput, and error rate for serving tests. Without these fields, do not present a precise server count or sustained tokens-per-second forecast.
Calculate model-weight memory
For an initial estimate, multiply parameter count by bytes per parameter:
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NVIDIA Dynamo’s planning factors are approximately:
| Serving or planning format | Approximate bytes per parameter | Planning interpretation |
|---|---|---|
| BF16 or FP16 | 2 bytes | Use for a first-order full-precision estimate |
| FP8 or INT8 | 1 byte | Use for a first-order eight-bit estimate |
| FP4 | 0.5 byte | Use for a first-order four-bit estimate |
Using those factors, the guide’s example puts a 70-billion-parameter model at approximately 140 GB in BF16 or 70 GB in FP8. Those figures describe the weights only; they do not include KV cache, activations, framework allocations, quantization scales, packing metadata, or reserved headroom.
Checkpoint files can also contain tensors that are converted, duplicated, or temporarily staged during loading. Runtime allocators may reserve blocks rather than exactly matching the arithmetic result. Therefore, use the multiplication as a capacity screen, then inspect the serving engine’s reported allocation on the target configuration.
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Estimate KV-cache memory from active tokens
The KV cache stores attention keys and values for tokens that remain available to later decoding steps. Its size grows with the number of layers, the number and dimension of KV heads, the cache data type, and the number of active tokens.
For a common transformer layout, NVIDIA gives this per-token form:
KV bytes per token ≈ 2 × layers × (attention heads × head dimension) × bytes per cache element
The factor of two accounts for one key tensor and one value tensor. For a simplified architecture in which attention heads × head dimension equals hidden size:
KV bytes ≈ batch size × sequence length × 2 × layers × hidden size × bytes per cache element
Multiply the per-token result by the total number of tokens whose cache is resident at the same time. That total is affected by concurrent requests, each request’s prompt and generated-token history, prefix-caching policy, and any batching or scheduling limits.
Worked KV-cache example
NVIDIA’s published example uses Llama 2 7B, FP16 cache elements, batch size one, and a 4,096-token sequence, producing approximately 2 GB of KV cache. This is a model- and configuration-specific example. It should not be applied to every seven-billion-parameter model: grouped-query attention, a different layer count, another head dimension, a different cache type, or multiple concurrent sequences changes the result.
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For grouped-query or multi-query attention, use the number of KV heads actually stored by the engine rather than substituting the full query-head count. If the backend exposes a cache-byte or block-usage calculation, prefer that accounting over the simplified hidden-size formula.
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A service with 32 simultaneous requests at 8,192 tokens can need far more cache than a service with one request at 4,096 tokens, even when both use the same weights. Maximum context settings can also reserve blocks that remain unused, depending on the backend’s allocation policy. Size for the active-token distribution you expect, then test the high-percentile and maximum cases that your service promises to accept.
Add runtime overhead and leave usable headroom
NVIDIA Dynamo presents this planning inequality:
weights + KV cache + overhead ≤ (tensor parallelism × pipeline parallelism) × VRAM per GPU
The guide recommends planning roughly 10–15% overhead. Treat that percentage as a planning recommendation, not a universal runtime constant. The actual amount depends on the framework, kernels, communication buffers, allocator behavior, graph capture, activation sizes, and enabled features.
A practical worksheet is:
- Compute weight bytes from parameter count and serving precision.
- Compute KV bytes for the active-token target and cache precision.
- Add expected framework, activation, communication, and loading allocations.
- Apply the 10–15% planning allowance or a measured allowance from the target runtime.
- Compare the total with usable—not merely advertised—VRAM.
- Keep additional operating margin if traffic bursts, longer prompts, or rolling model updates are possible.
Do not assign every remaining byte to KV cache. A server that technically fits at an allocator limit can experience preemptions or failed allocations when a request pattern changes.
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Tensor parallelism
Tensor parallelism partitions model computation and weight storage across GPU ranks. It can make a model fit when one GPU cannot hold a complete copy, but communication traffic and topology become part of the performance equation. NVIDIA recommends filling tensor parallelism within a node before using pipeline parallelism or multiple nodes, subject to the system’s interconnect and measured workload.
Pipeline parallelism
Pipeline parallelism places different model stages on different GPU ranks. It increases pooled capacity, but introduces stage scheduling and activation transfers. Pipeline bubbles or imbalanced stages can reduce effective throughput, so a fit calculation should be followed by a latency and throughput test.
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Data parallelism
Data parallelism replicates the model. It can increase aggregate throughput by serving independent requests on separate replicas, but it does not make a single model copy fit on a smaller GPU. First establish that one replica fits with its cache and overhead; only then use data-parallel replicas for capacity or throughput expansion.
Per-device fit versus total pooled memory
Summing VRAM across GPUs is meaningful only when the selected parallelism strategy can distribute the weights, cache, activations, and communication buffers as required. A pool with enough aggregate memory can still fail if one rank receives a larger allocation, if a layer cannot be partitioned as expected, or if the interconnect cannot sustain required transfers.
Use bandwidth specifications correctly
Record each GPU’s advertised memory bandwidth, but keep it separate from measured serving performance. NVIDIA’s A100 datasheet, dated June 2021, lists up to 80 GB of HBM2e and over 2 TB/s of GPU memory bandwidth for the A100 model family. Those are product specifications for that family, not universal figures for newer or different GPUs and not a throughput guarantee.
Bandwidth requirements vary by serving phase. Prefill processes the prompt and may expose substantial parallel computation and memory traffic. Decode advances one or a few tokens per sequence repeatedly; its efficiency is influenced by weight reads, KV-cache reads and writes, batch composition, and kernel behavior. The same GPU can therefore show different utilization and token rates for short prompts, long prompts, small batches, and high-concurrency decode.
When comparing hardware, ask for measurements under matching conditions rather than dividing a peak-bandwidth number by a model size. A useful comparison records:
- GPU memory capacity available after runtime reservations;
- peak memory bandwidth and the GPU model’s memory technology;
- GPU-to-GPU link type, topology, and cross-node path;
- supported weight and KV-cache precisions;
- measured prefill and decode throughput;
- time to first token, inter-token latency, and full request latency;
- concurrency, prompt/output distributions, backend, and software versions; and
- queue growth, preemptions, and memory headroom at the target load.
Account for topology and serving architecture
When model shards communicate frequently, the GPU-to-GPU path can matter as much as local HBM bandwidth. Keep tightly communicating tensor-parallel ranks on the fastest available links when the platform allows it. Crossing a host-memory or network path can add transfer latency and reduce effective throughput.
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Some deployments separate prefill and decode onto different GPU pools. This can help when the two phases have different resource profiles, but it also introduces KV-cache movement, routing, synchronization, and cache-locality decisions. Use that design only when measurements show a benefit large enough to justify the added operational complexity.
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Include host memory and storage tiers in the architecture diagram if the runtime can spill or stage data there. They may prevent an immediate out-of-memory error, but their bandwidth and latency are not equivalent to GPU HBM and should not be counted as interchangeable capacity for a latency-sensitive target.
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After the worksheet produces a candidate configuration, test the exact model, engine, versions, GPU count, topology, precision, and cache settings that will run in production.
- Build a representative request set. Preserve the observed distribution of prompt lengths, generated-token lengths, stop conditions, and concurrency. Include a separate stress case for the longest supported context.
- Warm the runtime. Load weights, complete graph capture or kernel compilation where applicable, and discard startup effects before collecting steady-state measurements.
- Measure prefill and decode separately. Record time to first token and inter-token latency, then record output-token throughput and end-to-end request latency.
- Sweep concurrency. Increase simultaneous requests until the latency objective, queue limit, or error-rate objective is exceeded. Record the point at which throughput stops scaling.
- Observe memory behavior. Track GPU memory used, KV-cache utilization, allocation failures, cache evictions, and any preemptions while the workload is running.
- Repeat after changing one variable. Compare precision, cache type, batch limits, parallelism, or GPU topology one at a time so that a result can be attributed correctly.
- Record the test envelope. Save GPU type, backend, software versions, model revision, request distribution, concurrency, latency, throughput, and error rate with every result.
A sizing estimate is complete only when the measured configuration meets both fit and service objectives. If a benchmark uses shorter prompts, lower concurrency, or a different cache precision than production, its result does not validate the production plan.
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Read cache and queue metrics as capacity signals
NVIDIA AIPerf documents runtime signals that expose memory pressure. For vLLM, vllm:kv_cache_usage_perc above 0.9 is flagged as near-full capacity, and nonzero preemptions indicate that requests are being interrupted or recomputed because memory is constrained. For SGLang, high token usage and growing queues are saturation signals. These are diagnostic cues tied to the selected backend and configuration, not universal service-level thresholds.
- High KV-cache utilization with acceptable latency: the workload may be using capacity efficiently, but there is little room for bursts or longer contexts.
- High utilization plus preemptions: reduce active tokens, lower concurrency, use a smaller cache footprint, add GPUs, or change the parallelism plan.
- Low cache utilization but poor throughput: investigate kernels, batching, synchronization, interconnect traffic, CPU input processing, and bandwidth utilization rather than adding VRAM alone.
- Growing queue with stable memory: the bottleneck may be compute, scheduling, admission control, or an undersized replica pool rather than cache capacity.
- Memory spikes during load or reload: inspect temporary checkpoint and allocator allocations; steady-state arithmetic may not cover lifecycle peaks.
Compare two server options with the same worksheet
Use identical model, precision, context, concurrency, backend, and software assumptions for every candidate. Keep advertised specifications and measured results in separate columns.
| Comparison axis | What to record | Why it affects the decision |
|---|---|---|
| Usable VRAM | VRAM remaining after weights, runtime allocations, and reserved headroom | Determines whether the target cache and request mix fit without preemption |
| Memory bandwidth | Vendor peak specification and measured utilization or throughput | Indicates data-movement potential but does not predict tokens per second alone |
| Interconnect and topology | Within-node links, cross-node path, rank placement, and host-memory path | Controls the cost of tensor/pipeline communication and KV movement |
| Precision support | Weight format, KV-cache format, kernels, and quantization metadata | Changes both capacity and arithmetic performance |
| Measured serving result | TTFT, inter-token latency, request latency, output throughput, concurrency, errors | Tests the actual service objective under a defined workload |
| Operational headroom | Cache utilization, queue depth, preemptions, and burst margin | Shows whether the configuration remains stable outside the average case |
An NVIDIA A100 80GB GPU is a concrete example of a capacity-and-bandwidth specification: the June 2021 datasheet lists up to 80 GB HBM2e and over 2 TB/s bandwidth. Its suitability for a particular inference service still depends on the model, runtime, topology, current availability, and measured workload behavior; the specification is not a blanket recommendation.
A compact estimation checklist
- Have you identified the exact checkpoint and parameter count?
- Are weight precision and KV-cache precision recorded separately?
- Did you calculate KV memory from active tokens, not only maximum context?
- Did you use the actual number of KV heads for grouped-query or multi-query attention?
- Did you reserve approximately 10–15% planning overhead, then verify the real allocator footprint?
- Does the selected tensor/pipeline strategy make the per-rank allocation fit?
- Are data-parallel replicas being counted as additional copies rather than pooled memory?
- Are GPU memory capacity, peak bandwidth, and interconnect topology listed separately?
- Were prefill, decode, concurrency, latency, throughput, and error rate benchmarked with the production request distribution?
- Did you monitor KV-cache utilization, GPU memory, preemptions, waiting requests, and queue time?
What the calculation can and cannot establish
The worksheet can tell you whether a proposed model and request envelope are plausibly within the memory budget and which variables drive that budget. It can expose why a long-context, high-concurrency service needs more VRAM even when its weights are unchanged, and it can prevent the common mistake of treating pooled data-parallel memory as one shared model store.
It cannot, by itself, establish sustained throughput, latency at a percentile, a final GPU count, or a server configuration. Those outcomes require a benchmark using the target runtime, software versions, topology, precision, and request distribution. Until those inputs and measurements exist, present the result as a transparent estimate rather than a guaranteed capacity plan.
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