The Tool Desk
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What determines how much GPU memory an LLM needs?
A useful estimate separates memory into weights, KV cache, runtime overhead, and headroom. The first two are workload-dependent; the latter two depend heavily on the inference software and configuration.
- Weights: the model parameters loaded in the selected format, such as BF16 or a quantized representation.
- KV cache: stored attention keys and values for tokens in active sequences. It grows with sequence length and concurrency.
- Runtime allocations: memory for activations, CUDA context and graphs, communication buffers, adapters, and modality-specific state where applicable.
- Headroom: space for allocation peaks and memory use that profiling or a simple estimate may not capture.
NVIDIA’s NIM memory troubleshooting guide describes a budget that accounts for weights, non-Torch overhead, peak activations, KV cache, and separate allocation headroom. A checkpoint loading successfully does not prove a serving workload will fit: TensorRT-LLM documents cases where engine building succeeds but runtime allocation of large I/O tensors, including KV cache, can fail.
How to estimate memory for your model and workload
1. Identify the exact model configuration
Record the parameter count, number of layers, hidden size or KV-head dimensions, and any multimodal or adapter components. Check the model card and configuration rather than relying on a model-family label. NVIDIA notes that parameter counts may also be available in safetensors index metadata.
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2. Estimate weight memory at your chosen precision
For a first estimate, use:
weight memory per GPU ≈ total parameters × bytes per parameter ÷ tensor-parallel degree
NVIDIA’s rough values are about 2 bytes per parameter for BF16/FP16, 1 byte for FP8, and 0.5 byte for INT4. These are estimates, not exact checkpoint sizes: quantization scales, alignment, implementation details, and other allocations affect actual use. Tensor parallelism distributes weights across devices, so dividing by its degree gives a rough per-GPU weight estimate, not a full deployment-memory guarantee.
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For example, NVIDIA estimates that an 8-billion-parameter BF16 model needs about 16 GB for weights. Its guide says this can fit on one 24 GB GPU, such as a GeForce RTX 4090, with some room for cache and overhead. The remaining capacity depends on the request lengths, serving configuration, and runtime allocations.
3. Estimate KV cache for context and concurrency
For common transformer architectures, NVIDIA gives this estimate:
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KV cache bytes ≈ batch size × sequence length × 2 × number of layers × hidden size × bytes per cache value
The factor of two represents keys and values. Sequence length includes input and generated tokens, and cache demand in this estimate grows with both sequence length and batch size. NVIDIA’s worked Llama 2 7B illustration estimates about 2 GB at batch size 1, sequence length 4096, and half-precision cache values. Treat that as a model-specific illustration, not a universal allowance.
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Some architectures, including grouped-query attention, use a different number of KV heads. In those cases, use the model’s actual KV layout and the runtime’s cache dtype and allocation method rather than assuming hidden size alone gives an exact result. Current vLLM serving documentation and TensorRT-LLM documentation describe cache controls and options, but support depends on the model, runtime, and hardware.
4. Add runtime needs and headroom
Allow memory beyond the weight and cache estimates for activations, CUDA graphs, communication buffers, LoRA adapters, or multimodal state when those apply. Actual allocation behavior varies by backend, so a calculation based only on parameter count and cache size can understate the requirement.
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5. Check the serving engine’s memory settings
In vLLM, the documented serving controls include GPU-memory-utilization-based budgeting, explicit KV-cache sizing, cache data types, and CPU offload. Review the options for the version you run; defaults and available controls can change. vLLM’s CLI guidance cautions that CPU offload relies on a fast CPU–GPU interconnect, so it is not a cost-free substitute for GPU memory.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much VRAM do common model sizes need?
There is no reliable universal VRAM number for a parameter count alone. Weight-only estimates are a starting point; they exclude cache, runtime allocations, and headroom. The examples below show how precision and device distribution change the weight estimate, not how much every serving workload requires.
| Example | Estimated weight memory | What the figure means |
|---|---|---|
| 8B model, BF16 | About 16 GB | NVIDIA NIM’s estimate for an 8-billion-parameter BF16 model; it may fit on one 24 GB GPU with some remaining room, depending on cache and overhead. |
| 70B model, BF16, 4-way tensor parallelism | About 35 GB per GPU | NVIDIA NIM’s estimate for distributed weights: 70 billion parameters × 2 bytes ÷ 4 GPUs. It is not a complete per-GPU serving budget. |
| 70B Llama 2, full precision | 256 GB | Weight-memory figure stated in Hugging Face’s Transformers inference guide; workload cache and runtime needs are additional. |
| 70B Llama 2, half precision | 128 GB | Weight-memory figure stated in the same guide; it does not include a serving workload’s full memory budget. |
The 70B examples are not contradictory: they describe different precision assumptions and, in NVIDIA’s example, weight distribution over four GPUs. For any row, the actual model configuration and inference setup determine whether the full workload fits.
How to choose between a larger GPU, quantization, and multiple GPUs
Compare options against the same model and serving target. Changing the hardware while also changing context length, concurrency, precision, and runtime makes the comparison difficult to interpret.
- More VRAM in one GPU: avoids splitting weights across devices, but the card still needs room for cache and runtime use. A 24 GB card is only an illustration for the 8B BF16 example, not a general recommendation.
- Lower-precision weights: reduce the weight estimate. Check that the chosen model and runtime support the representation, and validate quality and performance for your use. Hugging Face notes that quantization can slightly increase latency in some cases.
- Tensor or pipeline parallelism: distribute model weights across devices. This changes the deployment topology; consider the runtime and interconnect requirements as well as per-device memory.
- CPU offload: can reduce what must remain resident in GPU memory, but may affect performance and, in vLLM’s guidance, depends on a fast CPU–GPU interconnect.
- Shorter context or fewer simultaneous requests: reduce KV-cache demand in the common estimate. This is a serving-capacity trade-off: make sure the reduced limits still meet the application’s needs.
A practical checklist before selecting GPU capacity
- Pin down the model: use its actual configuration, parameter count, layer count, KV-head layout, and any adapters or multimodal components.
- Set the representation: identify weight precision and, separately, KV-cache dtype. Do not assume they are the same.
- Define the workload: specify maximum prompt plus output tokens and the intended concurrent requests or batch size.
- Choose the runtime: check its memory budgeting, cache allocation, supported dtypes, and offload controls for the version and hardware you plan to use.
- Calculate per-device needs: estimate weights after any distribution across GPUs, then add cache, runtime allocations, and headroom.
- Validate at the target settings: confirm the model runs under the intended context and concurrency; a successful load or engine build alone does not establish that runtime allocations will fit.
A reader-specific recommendation therefore needs the exact model and configuration, weight and cache precision, maximum prompt-plus-output length, concurrency target, inference runtime and version, other GPU workloads, and whether multiple GPUs or CPU offload are acceptable.
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