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What information do you need before sizing?
A capacity estimate is meaningful only for a defined serving workload. Write down the model artifact and revision, its architecture and parameter count, the weight precision or quantization, and the serving backend and version. Also specify the context and concurrency profile: typical and maximum input and output tokens, concurrent sequences, target throughput, and latency objectives.
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Include deployment details that change the memory or loading profile: whether you use adapters, multimodal inputs, or hybrid-model state; the GPU topology and parallelism plan; artifact size; and how quickly a new instance must become ready after a deployment or failure. Without these inputs, a GPU or SSD recommendation is a scenario, not a requirement. NVIDIA’s NIM memory guidance and Google Cloud’s GKE inference guidance both frame sizing around the deployed model and serving configuration.
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How much GPU memory do model weights need?
Start with NVIDIA’s documented heuristic for weight memory per GPU:
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Weight memory per GPU ≈ total parameters × bytes per parameter ÷ tensor-parallel degree
This is a first-pass estimate of the weights, not a complete serving-memory requirement. The bytes-per-parameter values below are NVIDIA’s guidance in documentation version 2.0.13, accessed in 2026; actual use depends on how the deployed artifact and backend represent the weights.
| Weight format in NVIDIA’s heuristic | Estimated bytes per parameter |
|---|---|
| BF16 or FP16 | 2 |
| FP8 | 1 |
| INT4 or NVFP4 | 0.5 |
For example, NVIDIA estimates the following weight footprints. These are vendor calculations, not independent benchmark results, and do not establish that the remaining GPU memory will be sufficient for a serving profile.
| Model example | Precision | Tensor parallelism | Estimated weight memory per GPU |
|---|---|---|---|
| Llama 3.1 8B | BF16 | TP=1 | 16 GB |
| Llama 3.3 70B | BF16 | TP=4 | 35 GB |
| Llama 3.3 70B | FP8 | TP=2 | 35 GB |
Use the estimate to identify candidate configurations, then verify the actual model’s loading behavior and memory use with the intended runtime. A model’s nominal parameter count does not account for every allocation or implementation detail.
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What else uses GPU memory besides weights?
A usable per-GPU budget must also leave room for KV cache, peak activations, communication buffers, CUDA context, graph capture and other runtime allocations. Adapters, multimodal inputs, or hybrid-model state may add further requirements. Startup logs and the effective runtime configuration help show what is actually allocated; memory accounting can vary by backend and version. NVIDIA documents memory troubleshooting in its NIM guide, and its NIM configuration guide describes configuration options for that product and version.
Estimate KV-cache demand from context and concurrency
KV cache is workload-dependent: longer contexts and more concurrent sequences can increase its demand. Measure the service’s actual prompt and output distributions rather than budgeting from a single maximum context or a model-size rule alone.
Google Cloud’s GKE serving article offers a planning heuristic of leaving about 20% of accelerator memory for KV cache after model weights, while noting that longer contexts may need more—up to 35% or more in its examples. These are provider examples, not a universal ratio; use the context lengths, concurrency, backend, and measured allocations of your own workload. See Google Cloud’s GPU-selection guidance for LLM serving on GKE.
Set a runtime memory limit with headroom
Google Cloud’s current GKE guidance describes tuning gpu_memory_utilization in the 0.9–0.95 range for its documented setup, and lowering it if out-of-memory errors occur. Treat that range as a GKE-specific operational starting point, not a portable default for other inference servers. A higher cache budget may support throughput only when runtime allocations and startup headroom remain safe; consult Google Cloud’s GKE inference best practices for its context.
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How many GPUs should you use?
If weights do not fit on one GPU with useful space left for cache and runtime allocations, test tensor parallelism or another sharding method supported by your serving stack. Do not assume that adding GPUs will make serving faster: tensor parallelism can add synchronization overhead, and pipeline parallelism can add latency. GPU topology and inter-GPU communication matter as much as the theoretical split. Google discusses these trade-offs in its GKE guidance for selecting GPUs for LLM serving.
Choose a configuration against the service objective, not weight fit alone. Compare time to first token, inter-token latency, request latency, throughput at the target concurrency, generated tokens per second, and error rate. Keep the workload profile defined earlier, model revision, backend and version, cache state, network configuration, and measurement method consistent between candidates. NVIDIA’s inference reference architecture recommends measuring both serving and model-loading behavior; a configuration that loads successfully may still miss latency or throughput goals.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much storage do model artifacts and serving need?
Plan storage by role and movement path instead of treating it as one large capacity number. NVIDIA’s Inference Reference Architecture maps object, file, block, and local ephemeral storage to different uses, but does not prescribe a universal SSD capacity, bandwidth, endurance, or cache policy.
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| Storage role | What belongs there | What to size or plan |
|---|---|---|
| Persistent artifacts | Versioned model weights, tokenizer and configuration files, and deployment artifacts | Total retained artifact versions, durability, and access requirements |
| Hot model cache | Frequently reused model or image data on a node-local or shared cache | Repeat-load behavior, cache-hit rate, deployment scale-out, and recovery objectives |
| Ephemeral working space | Temporary tensors, scratch space, and disposable local cache | Peak working-set size and behavior when a worker or its local storage is lost |
| Telemetry and benchmark output | Logs, metrics, traces, and benchmark reports | Write volume, retention, access, and whether short-lived data can be local |
Local NVMe can be a tier for model or image cache, temporary tensors, and short-lived logs; that makes it a possible component, not a universal requirement. Derive capacity and performance needs from artifact size, simultaneous starts, cache hit rate, write volume, recovery expectations, and provider limits. For any SSD-backed cache or offload, account for wear, failure behavior, cache ownership, transfer, eviction, recovery, and observability in the operating plan. NVIDIA’s reference architecture calls out these concerns but does not establish a one-size-fits-all drive specification.
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How should you benchmark loading and serving?
Measure startup separately from steady-state inference. A benchmark that begins with a warm cache or uses a different software revision may not predict a cold production restart. Track the stages that matter to your deployment:
- Artifact access: record download time or cache-hit time.
- Loading: measure disk-to-GPU movement and peer transfers where applicable.
- Initialization: record container startup, backend initialization, and time until the service is ready to accept requests.
- Serving: measure first-token time, inter-token latency, request latency, generated tokens per second, throughput at target concurrency, and errors under a representative request mix.
- Recovery: repeat the load measurement for the cache state and restart path relevant to deployment or failure recovery.
Use the same model revision, runtime stack, hardware configuration, network mode, and workload profile when comparing candidates. Record the cache condition for each run and retain the results alongside the configuration; otherwise, a difference in cache warmth or software revision can be mistaken for a hardware effect. NVIDIA’s inference reference architecture covers artifact discovery, cache warmup, weight movement, initialization, and readiness as distinct considerations.
What should the final sizing decision include?
There is no vendor-supported universal GPU count or SSD capacity for “an LLM” without a specific workload. The useful deliverable is a tested configuration and its assumptions. Include the selected artifact and precision, per-GPU weight estimate, measured remaining memory for cache and runtime, GPU topology and parallelism, storage roles and cache behavior, and load and serving measurements. Record the operational trade-offs too: recovery time, SSD wear and failure handling, and the complexity of the chosen storage and sharding design.
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