A 1-trillion-parameter model needs roughly 500 GB to 2 TB of GPU memory just for its weights, depending on whether they are stored at 4-bit or 16-bit precision. That is a lower bound, not a full runtime budget: inference also needs memory for the KV cache, activations and runtime buffers. The weights alone are far beyond a single GPU’s capacity, so running a model at this scale requires multiple GPUs.
How much memory do the weights require?
The quick estimate is parameter count multiplied by bytes per parameter. NVIDIA’s NIM documentation lists 2 bytes per parameter for BF16 or FP16, 1 byte for FP8, and 0.5 byte for INT4 or NVFP4. For one trillion parameters, that works out to:
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| Weight format | Bytes per parameter | Weight storage for 1T parameters |
|---|---|---|
| BF16 or FP16 | 2 | 2 TB |
| FP8 | 1 | 1 TB |
| 4-bit INT4 or NVFP4 | 0.5 | 500 GB |
These are decimal units: 1 TB is 1,000,000,000,000 bytes, and 1 GB is 1,000,000,000 bytes. The calculation counts only the weight values; quantization metadata can add some storage beyond the simple 4-bit estimate. Lower-precision formats reduce the weight footprint, but the table does not establish the quality trade-off for a particular model.
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During inference, a serving system also allocates memory for activations, input/output tensors, communication, and runtime functions such as CUDA graphs. The KV cache—the stored attention state for tokens in active requests—can be a major additional cost. Its size depends on the model architecture and serving setup, including context length and the number of concurrent users. TensorRT-LLM’s memory documentation describes these contributors and notes that settings such as batch size, input length, beam width and token limits affect allocations.
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One NVIDIA illustration puts the KV cache for a single 128K-token Llama 3 70B request at about 40 GB; cache use in that example scales with the number of users. This is a model-specific example, not a sizing figure to apply to a different architecture. NVIDIA’s KV-cache discussion helps show why context and request concurrency can add substantially to weight storage.
For planning, treat the weight calculation as a floor. The actual memory target depends on the model, context lengths, expected concurrent requests, serving configuration and desired performance headroom.
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Can a 1-trillion-parameter model run on one GPU?
No—not with the weight footprints above. The weights alone require at least 500 GB even at the listed 4-bit representation, before cache and runtime allocations. The model must be partitioned across multiple GPUs, and the number required depends on the precision, each GPU’s usable memory and the serving workload.
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NVIDIA’s 1.8T-parameter MoE example assumes a budget of 64 GPUs with 192 GB each and says that FP4’s half-byte-per-parameter representation needs a minimum of five GPUs just to hold the parameters. NVIDIA presents that as a modeled example, not a deployment recipe for a 1T model; it also notes that a larger GPU allocation can improve the user experience. Fitting weights is not the same as having enough capacity for useful throughput, context or concurrency.
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What matters when sizing a multi-GPU deployment?
Aggregate GPU memory is only one part of the decision. NVIDIA explains that tensor parallelism splits model layers across GPUs, while scaling it without high-bandwidth GPU networking can introduce communication bottlenecks. Compare a proposed setup against these workload needs:
- Weight precision: Estimate the weight footprint for the format the model actually supports, and account for the quality trade-off relevant to that model.
- KV-cache capacity: Size for the intended context length and number of simultaneous requests, rather than weights alone.
- Interconnect and parallelism: Check that the GPU-to-GPU communication path suits the way the model is partitioned.
- Runtime headroom: Leave capacity for activations, buffers and other allocations beyond the weights.
- Performance target: A configuration that can load the model may still fall short of the required response time or throughput.
As one vendor-specific scale reference, NVIDIA specifies 13.4 TB of HBM3E across the 72 GPUs in its GB200 NVL72 system. That is a specification for that rack-scale system, not a generic minimum requirement for a trillion-parameter model.
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How is training memory different?
The figures above estimate inference weight storage, not the memory needed to train the model. Training also requires memory for training state and computation; the total depends on the optimizer, precision policy, sharding, activation handling, and sequence and batch choices. NVIDIA’s mixed-precision training guide describes using FP16 where appropriate with loss scaling to reduce memory versus FP32. Without a defined training configuration, there is no single reliable total-memory figure to apply to every 1T-parameter model.
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