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Capacity and bandwidth answer different questions
Memory capacity asks “will it fit?” The deployment must hold the model’s parameters, resident key-value (KV) cache, activation and input/output tensors, framework allocations, communication buffers, and operating headroom. Running out of capacity causes allocation failures, cache evictions or preemptions, and queues that grow even when the arithmetic workload is modest.
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Memory bandwidth asks “how fast can data move?” During inference, the GPU repeatedly reads weights and cache entries and writes intermediate results. Higher peak bandwidth can help a bandwidth-bound workload, but realized performance also depends on attention shape, sequence lengths, precision, batching, cache locality, kernel implementation, interconnects, and software versions. Capacity and bandwidth therefore need separate calculations and a common benchmark.
The NVIDIA Dynamo deployment guide states the practical fit test 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.” The phrase “pooled GPUs” matters: splitting a model across devices can make one copy fit, but it does not reduce the total bytes required by that copy.
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Collect workload inputs before choosing hardware
A sizing worksheet is only as good as its assumptions. Record these values for the service you actually intend to run:
- Model architecture and checkpoint, including parameter count, number of layers, attention heads, head dimension, and any grouped-query or multi-query attention variant.
- Checkpoint and serving precision for weights, activations, and KV cache (for example, BF16, FP16, FP8, INT8, or FP4).
- Maximum and typical prompt lengths, generated-token lengths, and the distribution of total active tokens.
- Maximum and normal concurrency, batch policy, admission limits, and whether requests share prefixes.
- Target time to first token, inter-token latency, end-to-end latency, output-token throughput, and acceptable error or timeout rates.
- Serving backend, version, quantization implementation, GPU type, number of GPUs, GPU-to-GPU interconnect, host-memory path, and storage tier.
- Whether prefill and decode run on the same pool or on separate GPU pools.
Separate prefill (processing the prompt) from decode (generating one or a few tokens at a time). They exercise memory traffic and batching differently, so an average token rate can hide a prompt-latency problem or a decode-bandwidth problem.
Estimate model-weight memory
For an initial budget, multiply the parameter count by the number of bytes used by each stored parameter:
weight bytes ≈ parameter count × bytes per parameterNVIDIA Dynamo version 1.5.0 uses these planning factors:
| Serving representation | Planning factor | Illustrative weight estimate |
|---|---|---|
| BF16 or FP16 | about 2 bytes per parameter | 70B parameters ≈ 140 GB |
| FP8 or INT8 | about 1 byte per parameter | 70B parameters ≈ 70 GB |
| FP4 | about 0.5 byte per parameter | 70B parameters ≈ 35 GB by the same rough factor |
The 70B figures are planning examples, not complete serving footprints. Checkpoint headers, scale factors, quantization metadata, packing, alignment, duplicated weights, and runtime allocations can make the resident allocation larger or differently shaped. A 7B model would be approximately 14 GB at the same BF16/FP16 factor or 7 GB at the FP8/INT8 factor, before cache and overhead; treat those as arithmetic estimates rather than measured allocations.
Do not allocate every free byte to weights. A model that technically loads but leaves no room for cache growth, temporary tensors, CUDA graphs, communication buffers, or fragmentation is not production-ready.
Calculate KV-cache capacity
The KV cache stores attention keys and values for tokens that remain available to later decode steps. It is often the largest variable memory consumer after the weights because it grows with resident tokens, not merely with model size.
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For a common transformer layout, NVIDIA gives this per-token estimate:
KV bytes per token ≈ 2 × layers × (attention heads × head dimension) × bytes per cache elementThe factor 2 accounts for keys and values. If attention heads × head dimension equals the model hidden size, a convenient batch form is:
KV bytes ≈ batch size × sequence length × 2 × layers × hidden size × bytes per cache elementMultiply the per-token result by the number of tokens whose cache is resident across all active requests. A long-context request and many short requests can consume the same total token budget, so concurrency and context length must be modeled together.
Worked reference example
NVIDIA’s published example for Llama 2 7B, FP16 cache, batch size 1, and a 4,096-token sequence yields approximately 2 GB of KV cache. That number belongs to that model and configuration; it is not a universal “7B cache size.” Changing layer count, attention layout, cache precision, batch, or context changes the result.
Account for modern attention layouts
Grouped-query and multi-query attention use fewer key/value heads than query heads. In those architectures, blindly substituting hidden size into the simplified formula can overstate cache use. Use the model’s actual KV-head count and the serving engine’s cache accounting. Also verify whether the backend stores cache in a different precision from the weights, uses block-based allocation, or reserves blocks for scheduler policy.
Model active tokens, not only maximum context
A maximum context setting is a limit, not an allocation forecast. Build at least three scenarios: typical traffic, a high-percentile prompt/output mix, and the admission limit with maximum concurrent tokens. If the service accepts long sessions, budget for their generated tokens remaining resident while new requests arrive.
Add framework, activation, and safety headroom
NVIDIA Dynamo recommends planning roughly 10–15% overhead and leaving a healthy remainder for KV cache. This is a guide recommendation, not a universal runtime constant. The actual amount depends on the backend, graph capture, temporary workspaces, quantization kernels, allocator fragmentation, communication buffers, and whether multiple models share a device.
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Use this inequality to expose the fit decision:
weights + KV cache + framework/activation overhead
≤ (tensor parallelism × pipeline parallelism) × usable VRAM per GPUUse usable VRAM, not the label on the product. Reserve space for the runtime and operating environment, then test whether the remaining pool supports your active-token target. If the left side exceeds the right side, reduce context or concurrency, use a smaller or more aggressively quantized representation, add GPUs, or change the parallelism plan. Do not “solve” the problem by counting data-parallel replicas as extra capacity for one model copy.
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Tensor parallelism
Tensor parallelism partitions portions of a layer across GPU ranks, allowing one model copy to span devices. It can lower the per-device weight requirement, but it introduces all-reduce or other collective traffic across the interconnect. NVIDIA recommends filling tensor parallelism within a node before reaching for pipeline parallelism or multiple nodes, subject to the topology and measured workload.
Pipeline parallelism
Pipeline parallelism places different layer ranges on different ranks. It can extend the pooled capacity, but bubbles, activation transfers, and scheduling complexity may reduce utilization, especially for small batches or latency-sensitive requests.
Data parallelism
Data parallelism replicates the model. It increases aggregate request throughput when one replica already fits; it does not make a single copy fit on a smaller pool. Count each replica’s weights and its own cache when planning total capacity.
Multiple nodes
Cross-node partitioning adds network latency and bandwidth requirements. Verify collective performance, placement, failure behavior, and whether the serving engine supports the chosen topology. A theoretical pooled-memory total is not enough if transfers prevent the service from meeting latency objectives.
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Estimate and interpret memory bandwidth
Start by recording the GPU’s advertised peak memory bandwidth, then treat it as an upper-bound hardware input rather than a throughput promise. A rough traffic view is:
required effective bandwidth ≈ bytes moved by the workload ÷ elapsed execution timeFor a token-rate target, you can reason about the implied traffic per token, but the byte count must come from the actual kernels and cache behavior. Weight reuse, batching, fused operations, cache hits, precision, and rereads all change the traffic. Consequently, a simple “model size × tokens per second” calculation is at best a screening estimate, not a measured bandwidth requirement.
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Capacity and bandwidth are separate specifications
| Specification or result | What it tells you | What it does not tell you |
|---|---|---|
| VRAM capacity | How much model, cache, and runtime state can reside on the device or pooled devices | Whether kernels can move data fast enough for a latency or token-rate target |
| Peak memory bandwidth | Theoretical maximum device-memory transfer rate under the vendor’s conditions | Effective bandwidth for your model, precision, sequence mix, or backend |
| GPU-to-GPU link and topology | How quickly partitions and collectives can exchange data | Whether the application will actually use the link efficiently |
| Measured tokens per second and latency | Observed service behavior under a stated workload | Performance under a different prompt, output, concurrency, or software version |
As a dated hardware example, the NVIDIA A100 datasheet from June 2021 lists up to 80 GB of HBM2e and more than 2 TB/s of GPU memory bandwidth. Those figures describe the A100 model family; they are not a current universal recommendation and do not establish an inference throughput for any particular model.
Include topology and serving architecture
Record whether GPUs are connected by a high-speed peer fabric, PCIe, or a network link, and whether the runtime can access peer memory directly. A model that fits in aggregate VRAM may still miss its latency target if tensor-parallel collectives traverse a slower path.
Some systems place prefill and decode on separate GPU pools. This can isolate prompt bursts from decode latency, but KV data may move between GPU memory, host memory, and storage. Every extra tier adds transfer time, capacity accounting, and cache-locality questions. Introduce such a split only after measurements show that a single pool cannot meet the service objective or that isolation materially improves it.
Worked planning examples
70B weights in FP8
- Apply the Dynamo planning factor: 70B parameters × 1 byte ≈ 70 GB of weights.
- Subtract the weight estimate from the pooled usable VRAM, not the product’s nominal total.
- Reserve the remaining space for KV cache, activations, framework allocations, and the guide’s roughly 10–15% planning overhead.
- Test the active-token scenarios. A configuration that loads 70 GB of weights may still reject long-context or high-concurrency traffic when the cache grows.
70B weights in BF16
The same rough method gives approximately 140 GB of weights. A single 80 GB device cannot hold that weight set, even before cache and overhead; a multi-GPU tensor- or pipeline-parallel pool may, provided its interconnect and runtime support the arrangement. Data-parallel replication would require another complete copy per replica.
7B model with a 4,096-token request
For Llama 2 7B in the published FP16, batch-one, 4,096-token example, the KV cache is approximately 2 GB. Add that cache to the model’s weight estimate, runtime overhead, and any other resident requests. If ten such sessions are simultaneously resident, do not simply multiply the model weights by ten; the weights are shared within a replica, while the per-session KV allocations accumulate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Validate the estimate with a reproducible benchmark
The calculation narrows the hardware choices. The benchmark decides whether the service objective is met. Use the target checkpoint, precision, backend, software versions, GPU configuration, and topology; changing any of these can invalidate a comparison.
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- Warm up the service. Allow compilation, graph capture, memory pool creation, and cache initialization to settle before collecting measurements.
- Measure latency and throughput together. Capture time to first token, inter-token latency, request latency, output-token throughput, achieved concurrency, error rate, and queue time.
- Observe memory behavior. Record GPU memory used, KV-cache utilization, allocation failures, evictions or preemptions, waiting requests, and peak values during bursts.
- Repeat at several loads. Increase concurrency until latency objectives or error budgets fail. The knee of the curve is more useful than a single maximum tokens-per-second number.
- Publish the test conditions. Keep the GPU type, backend and version, precision, model revision, topology, prompt/output distributions, and concurrency beside every result.
NVIDIA’s reference architecture recommends recording time to first token, inter-token latency, request latency, output throughput, concurrency, error rate, prompt/output distributions, backend, GPU type, and software versions. Without those fields, two “tokens per second” claims may not be comparable.
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Read cache and queue metrics as capacity signals
AIPerf documents operational signals for common serving backends. For vLLM, vllm:kv_cache_usage_perc above 0.9 is flagged as near full capacity, and nonzero preemptions indicate memory pressure. For SGLang, high token usage together with growing queues is a saturation warning. Treat these as diagnostic cues for the selected backend and configuration, not universal service-level thresholds.
- High KV usage with preemptions: reduce active tokens, shorten the admission limit, lower cache precision if quality permits, or add pooled capacity.
- Low KV usage but high latency: investigate bandwidth utilization, kernel efficiency, synchronization, interconnect traffic, CPU scheduling, and request-shape imbalance rather than adding VRAM alone.
- Growing queue with moderate device memory: check arrival rate, scheduler limits, prefill bursts, host-to-device transfers, and whether the workload is compute- or bandwidth-bound.
- Memory spikes only during startup or reload: account for duplicate checkpoints, temporary conversion buffers, graph capture, and allocator fragmentation in the deployment procedure.
Compare server options on usable, measured attributes
When evaluating two or more systems, keep advertised specifications separate from measured results. Use the same model, workload, software, and acceptance criteria on every option.
| Comparison axis | Questions to answer |
|---|---|
| Usable VRAM | How much remains after weights, runtime reservations, communication buffers, and safety headroom? |
| Memory bandwidth | What is the vendor’s peak figure, and what effective bandwidth is observed under the target prompt/output mix? |
| Interconnect and topology | Are tensor-parallel collectives local to a node, and what path handles cross-node traffic? |
| Precision and cache support | Does the backend support the required weight and KV-cache formats without unexpected conversions? |
| Measured service performance | Under matching conditions, what are time to first token, inter-token latency, request latency, throughput, and concurrency? |
| Operational headroom | How close do cache usage, queue depth, memory use, and preemptions get to their limits during bursts? |
An NVIDIA A100 80GB GPU can be used as a concrete example when documenting this comparison: its June 2021 datasheet provides the 80 GB HBM2e and over-2-TB/s specifications. Whether it is suitable for a particular deployment still depends on the model, topology, software, availability, and measured workload results.
Common sizing mistakes and recovery steps
Assigning all VRAM to weights
Symptom: the model loads, then requests fail as contexts grow. Recovery: reserve cache and framework headroom before setting the admission limit; validate with active-token scenarios.
Using parameter count as a complete memory estimate
Symptom: a nominal “70B × 1 byte” calculation underestimates the deployed footprint. Recovery: add quantization metadata, packing effects, temporary allocations, communication buffers, and the selected cache representation.
Confusing maximum context with normal capacity
Symptom: a configuration passes a short test but collapses under long sessions. Recovery: model the joint distribution of prompt length, generated length, and concurrency, including the admission limit.
Counting data-parallel GPUs as pooled memory
Symptom: several replicas exist, but none can load the model. Recovery: size one complete model copy first; use tensor or pipeline parallelism when the copy must be partitioned.
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Choosing by peak bandwidth alone
Symptom: the specification looks faster, but latency is unchanged or worse. Recovery: profile effective bandwidth, kernel time, synchronization, cache behavior, and interconnect traffic with the target runtime.
Reporting an unqualified tokens-per-second result
Symptom: a result cannot be reproduced or compared. Recovery: publish model revision, precision, backend and versions, GPU topology, concurrency, prompt/output distributions, latency metrics, and error rate alongside throughput.
What a defensible capacity plan contains
- A weight estimate tied to the exact parameter count and serving representation.
- A KV-cache estimate based on layers, KV heads, head dimension, cache precision, and active tokens.
- An explicit overhead and headroom allowance rather than a nominal-VRAM total.
- A parallelism map showing which memory is partitioned and which is replicated.
- A bandwidth and topology description that distinguishes peak specifications from effective measurements.
- A benchmark plan with workload distributions, latency objectives, throughput, queue behavior, cache utilization, and preemptions.
- A stated uncertainty boundary: without the checkpoint, precision, context distribution, concurrency, runtime version, topology, and benchmark results, no precise GPU count or sustained throughput forecast is justified.
The result is a transparent estimate that can be recalculated when the model or traffic changes, rather than a one-time hardware guess. Capacity determines whether inference can run; bandwidth, topology, and software determine how well it runs under the workload that matters.
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