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Estimate inference capacity in two separate passes: first prove that the model, KV cache, and runtime allocations fit in usable VRAM; then benchmark the GPU configuration against your real prompt lengths, output lengths, concurrency, cache behavior, and latency targets. Weight memory is only the starting point, and no universal GPU count can satisfy every workload.
What a useful inference-sizing estimate must answer
A sizing exercise should answer two different questions:
- Will the model fit? This is a memory-capacity question involving weights, KV cache, activations, communication buffers, CUDA graphs, adapters, multimodal state, and other backend allocations.
- Will the service be fast enough? This is a compute and serving-capacity question involving prefill and decode behavior, time to first token (TTFT), inter-token latency, throughput, request rate, and the number of concurrent requests.
A configuration can pass the first test and fail the second. Conversely, a fast GPU is not useful if the model and its maximum working set cannot fit without unsafe memory pressure.
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NVIDIA summarizes the main memory contributors this way: “In effect, the two main contributors to the GPU LLM memory requirement are model weights and the KV cache.” That statement comes from NVIDIA’s Mastering LLM Techniques: Inference Optimization technical article. In production, however, the cache and the non-weight allocations must be budgeted rather than treated as optional extras.
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Define the workload before choosing a GPU
Record these inputs for the exact checkpoint and serving stack you intend to deploy. “An 8B model” is not a complete sizing specification: quantization, context limits, backend version, and traffic shape can change the result materially.
- Model and checkpoint: record the exact model family, revision, architecture, number of parameters, number of layers, hidden size, attention configuration, and any adapters or multimodal components.
- Weight precision: specify BF16, FP16, FP8, INT4, NVFP4, or the precise quantized profile supplied by the model publisher.
- Cache precision: the KV cache can use a different precision from the weights, depending on the runtime.
- Context requirement: separate maximum prompt length from the maximum total sequence length (prompt plus generated output).
- Output length: estimate the normal and high-percentile number of generated tokens.
- Concurrency: state both average simultaneous sequences and the peak concurrency you intend to support.
- Cache behavior: document prefix-cache or prompt-cache hit rates instead of assuming every request starts from an empty cache.
- Service targets: define TTFT, inter-token latency (also called token-per-output-token or TPOT in some tools), tail-percentile limits, aggregate output tokens per second, and requests per second.
- Deployment topology: note GPU VRAM, GPU count per node, NVLink or other interconnects, host-memory limits, and whether capacity is on-premises, reserved, or elastic.
Chatbots, coding copilots, long-running agents, content-generation systems, and translation services produce different token patterns. NVIDIA’s 2026 workload-sizing guidance includes illustrative categories such as agent workloads with cached input above 128,000 tokens. Those ranges are vendor examples, not universal population statistics, so use your own traces whenever possible.
Calculate model-weight memory
For a first-pass estimate, multiply the parameter count by the number of bytes used to store each parameter:
weight_memory_per_gpu ≈ total_parameters × bytes_per_parameter ÷ tensor_parallel_size
| Weight format | Approximate bytes per parameter used in NVIDIA’s heuristic | What the figure means |
|---|---|---|
| BF16 | 2 | Rough storage estimate before cache and runtime allocations |
| FP16 | 2 | Rough storage estimate before cache and runtime allocations |
| FP8 | 1 | Approximate weight storage; validate the actual model profile |
| INT4 or NVFP4 | 0.5 | Approximate packed weight storage; scales and runtime details add overhead |
For example, 8 billion parameters at BF16 are approximately 16 GB of weight storage. NVIDIA’s NIM documentation uses an 8B BF16 example and says it fits on a 24 GB GPU such as an A10G or GeForce RTX 4090 with room for KV cache and overhead. That is an official fit illustration, not a current purchase recommendation or a guarantee for every backend, context length, or concurrency level.
Similarly, 7 billion parameters at 16-bit precision are approximately 14 GB before cache and other allocations. These are arithmetic estimates, not measurements of a running process. Check the checkpoint’s actual tensor types, packed-quantization metadata, and startup allocation logs.
Estimate KV-cache memory
During autoregressive generation, the server retains keys and values for prior tokens so it does not recompute the entire sequence for every new token. For the common transformer architecture shown in NVIDIA’s guidance, use:
KV cache bytes ≈ batch_size × sequence_length × 2 × num_layers × hidden_size × bytes_per_cache_value
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The factor of two accounts for the key and value tensors. In this formula, batch_size is the number of active sequences, and sequence_length should reflect the tokens retained by the runtime, including the portion of the prompt and generated output that remains in context.
NVIDIA’s worked example for Llama 2 7B uses batch size 1, sequence length 4,096, 32 layers, hidden size 4,096, and an FP16 cache. It produces approximately 2 GB of KV-cache memory. Doubling either the active sequence count or the retained sequence length approximately doubles that cache estimate, all else being equal.
Do not apply the formula blindly to every architecture. Grouped-query attention and multi-query attention reduce the number of distinct key/value heads, and some runtimes use paged allocation, compressed caches, or other implementation-specific layouts. Read the model configuration and backend documentation to determine whether the published hidden-size form needs an architecture-specific adjustment.
Add runtime allocations and a safety margin
A practical memory budget is:
weights + KV cache + activations + communication/runtime overhead ≤ usable GPU VRAM
Non-weight consumers can include:
- Peak prefill and decode activations
- CUDA context and CUDA graphs
- Tensor- and pipeline-parallel communication buffers
- Adapter weights or other fine-tuning modules
- Multimodal encoders, image or audio buffers, and reserved workspace
- Hybrid-model state and backend-specific scheduling structures
Use usable VRAM, not the label printed on the product box. The CUDA driver, operating system, display server, monitoring tools, and serving process may reserve memory before the model is loaded. Allocation order and accounting also vary by model, backend, and version.
NVIDIA Dynamo’s version 1.5.0 deployment guidance gives an approximate 10–15% overhead allowance. Treat that as a rough planning aid, not a universal margin. A deployment with long contexts, many simultaneous sequences, graph capture, or multimodal buffers may require more. Confirm the real peak by inspecting startup logs, effective runtime configuration, and allocator statistics under representative load.
Check whether the model fits across multiple GPUs
For a model-parallel deployment, NVIDIA Dynamo states the pooled fit condition as:
weights + KV cache + overhead ≤ (tensor_parallel_size × pipeline_parallel_size) × VRAM_per_GPU
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This expression describes total pooled capacity; you must also verify that the chosen partition is legal for the model and leaves enough memory on each individual rank.
| Strategy | How it uses memory | Primary purpose | Important limitation |
|---|---|---|---|
| Tensor parallelism | Splits tensor operations and associated weights across GPUs | Make one model copy fit and accelerate its execution | Requires communication; topology and per-rank balance matter |
| Pipeline parallelism | Places different layer ranges on different GPUs | Extend capacity when one node or tensor-parallel group is insufficient | Introduces pipeline scheduling and inter-stage communication |
| Data parallelism | Replicates a complete model copy on each replica | Increase throughput after one copy already fits | Does not reduce the memory needed for a single copy |
Dynamo recommends filling tensor parallelism to the node width first when the node has fast intra-node NVLink, then considering pipeline parallelism or additional nodes. That is a topology-dependent deployment recommendation, not a rule that overrides your cluster’s interconnect, software support, or latency requirements.
Worked first-pass examples
8B BF16 model on one 24 GB GPU
- Estimate weights: 8 billion × 2 bytes = approximately 16 GB.
- Subtract that estimate from usable VRAM to identify the space available for KV cache, activations, graphs, communication buffers, and other state.
- Calculate the cache for the intended maximum sequence count and length. Do not size only for batch one if the service will admit concurrent requests.
- Load the exact model profile and inspect startup logs. The result is a fit check, not proof of the required latency or throughput.
7B-style transformer with a 4,096-token sequence
Using NVIDIA’s published assumptions—32 layers, hidden size 4,096, batch one, and FP16 cache—the KV cache is approximately 2 GB. If your service keeps four such sequences active, the same simplified calculation is approximately 8 GB before implementation-specific effects. A grouped-query model or a different cache precision can change that result.
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Inference has two very different phases:
- Prefill: the GPU processes the input prompt in parallel. Long prompts increase work and can increase TTFT, but the operation generally has more parallelism than decode.
- Decode: the model generates one token at a time for each active sequence. Performance can be limited by moving weights, keys, values, and activations through memory rather than by peak arithmetic throughput alone.
Track each metric separately:
| Metric | What it tells you | Why it matters |
|---|---|---|
| TTFT | Time until the first generated token | Captures prompt processing and queueing; important for interactive requests |
| Inter-token latency (ITL/TPOT) | Time between subsequent output tokens | Describes how quickly a streamed response continues |
| End-to-end latency | Total request time | Combines queueing, prefill, decode, and finalization |
| Output tokens per second | Aggregate generated-token rate | Useful for capacity planning across many requests |
| Requests per second | Completed request rate | Useful when requests have similar service-level objectives |
NVIDIA NIM benchmarking uses these categories, but metric implementations can differ. Compare results only when tokenization, warm-up, batching, cache policy, prompt and output lengths, percentile definitions, and runtime configuration are aligned.
Benchmark the configuration with representative traffic
- Build a traffic matrix. Include normal and high-percentile prompt lengths, output lengths, concurrent requests, cache-hit and cache-miss cases, and the expected mix of workload types.
- Fix the model profile. Record the checkpoint revision, quantization, cache precision, context limit, backend version, GPU type, tensor-parallel and pipeline-parallel settings, and graph-capture options.
- Warm up the service. Exclude model-loading and one-time initialization from steady-state results, but record startup memory and load failures separately.
- Measure low and high load. A single request reveals isolated latency; saturated tests reveal queueing, batching behavior, throughput, and tail latency.
- Capture TTFT, ITL, end-to-end latency, output tokens per second, requests per second, and peak VRAM. Report median and relevant tail percentiles rather than one average.
- Repeat after changing one variable. Compare precision, GPU count, parallelism, batch limits, cache policy, and context limits without changing the rest of the test at the same time.
- Test failure boundaries. Increase context length and concurrency until you observe out-of-memory errors, rejected requests, rising queue time, or a service-level objective violation.
Use the smallest configuration that satisfies both the memory boundary and the measured service targets, while retaining a margin for traffic spikes and model updates.
Compare candidate configurations on the right axes
| Decision axis | Questions to answer |
|---|---|
| Memory fit and cache headroom | Do weights, maximum retained context, peak concurrency, and non-weight allocations fit with a measured margin? |
| Latency | Does TTFT and inter-token latency meet the required percentile under realistic load? |
| Throughput | How many output tokens and completed requests per second are delivered at the target traffic mix? |
| Precision and quality | Does the selected checkpoint or quantization preserve application quality, not merely reduce bytes? |
| Scaling and topology | Will tensor or pipeline parallelism use the available interconnect efficiently, and is multi-node operation justified? |
| Cost and elasticity | What baseline capacity is reserved or operated on-premises, and what burst capacity is needed during peaks? |
For total-cost-of-ownership planning, combine measured capacity with utilization. A permanently provisioned cluster sized for rare peaks may cost more than a smaller baseline plus elastic burst capacity; a burst-only design may fail if demand is sustained or data-transfer and startup delays are unacceptable. Obtain current provider prices and availability separately because those values change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common sizing mistakes and how to correct them
Using only parameter count
Symptom: the arithmetic says the weights fit, but startup or higher concurrency causes an out-of-memory error. Correction: calculate KV cache at the actual context and concurrency, then add activations, communication buffers, graphs, adapters, multimodal state, and margin.
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Confusing total pooled VRAM with usable per-rank memory
Symptom: several GPUs appear to have enough aggregate capacity, but one rank fails during loading. Correction: verify the partition, per-rank allocation, layer balance, and communication workspace for the selected tensor- and pipeline-parallel topology.
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Assuming data parallelism makes a model smaller
Symptom: adding replicas does not solve a single-GPU out-of-memory error. Correction: use tensor or pipeline parallelism to shard one copy; use data parallelism only after one complete copy fits.
Setting context length without considering concurrency
Symptom: a single long request works, but several ordinary requests exhaust the cache. Correction: size the product of retained sequence length and active sequences, including scheduler limits and worst-case admission behavior.
Choosing from a hardware “tokens per second” headline
Symptom: published speed does not match production TTFT or streaming behavior. Correction: benchmark the intended model, prompt and output lengths, cache policy, concurrency, backend, and percentile target.
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Symptom: a deployment that looked safe fails after enabling CUDA graphs, adapters, or a new backend version. Correction: use the NVIDIA Dynamo figure only as a rough planning allowance and verify peak allocation in the exact runtime.
A reusable sizing worksheet
Fill in these values before requesting hardware or setting autoscaling limits:
- Model/checkpoint and parameter count: record the exact revision
- Weight precision and bytes per parameter: use the actual profile
- Cache precision and architecture-specific cache factor
- Maximum retained sequence length
- Average and peak active sequences
- Estimated weight memory
- Estimated KV-cache memory
- Activation, graph, communication, adapter, multimodal, and other runtime allowance
- Usable VRAM per GPU
- Tensor-parallel size and pipeline-parallel size
- Target TTFT, inter-token latency, tail percentile, output-token rate, and request rate
- Measured peak VRAM and benchmark results at representative load
Re-run the worksheet when the model revision, quantization, context limit, backend, GPU type, parallelism, or traffic distribution changes. A sizing result is valid only for the assumptions recorded with it.
Final decision rule
Approve a configuration only when the exact model profile fits within usable pooled VRAM with measured runtime headroom, and representative benchmarks meet the required TTFT, inter-token latency, throughput, and tail-latency targets. If it fits but is too slow, change the GPU, parallelism, batching, precision, or replica count. If it is fast enough but runs out of memory, reduce the working set or shard the model rather than relying on a larger nominal GPU count without checking per-rank allocation.
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