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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsTo estimate whether an LLM workload fits on a GPU, add three things: model weights, the key-value (KV) cache for the tokens and sequences you plan to serve, and an explicit allowance for runtime allocations. The JavaScript below estimates those amounts in GiB, including a per-GPU weight estimate for tensor parallelism. Treat it as a capacity-planning screen, not a promise of peak runtime memory: the model architecture, quantization format, and inference engine all affect the result.
What the estimate includes
Model weights are only the starting point. NVIDIA describes the two main contributors to GPU LLM memory as weights and KV cache, but a serving process also needs memory for activations, communication and workspace buffers, CUDA graphs, I/O tensors, and other runtime allocations. Those requirements vary with the runtime and workload, so the script keeps headroom explicit rather than assuming a universal percentage.
- Weights: parameter count multiplied by effective stored bytes per weight.
- KV cache: cache values for keys and values across layers, KV heads, retained tokens, and concurrent sequences.
- Runtime headroom: an amount you choose for allocations the two formulas do not model.
For a rough precision heuristic, NVIDIA NIM lists 2 bytes per parameter for BF16 or FP16, 1 byte for FP8, and 0.5 byte for INT4 or NVFP4. These are planning values, not guarantees of exact file size: quantization formats and implementations can add overhead. See NVIDIA NIM’s LLM performance documentation.
Use this 15-line JavaScript estimate
Set the inputs to match the model and workload. The example values below are illustrative only; replace them with the model’s parameter count and architecture configuration, the cache data type, and your intended serving load.
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const parameters = 7e9, weightBytes = 2, tensorParallelGpuCount = 1;
const layers = 32, kvHeads = 32, headDim = 128, kvBytesPerValue = 2;
const batch = 1, cachedTokens = 4096, runtimeHeadroomGiB = 2;
const weightsPerGpu = parameters * weightBytes / tensorParallelGpuCount;
const kvBytes = batch * cachedTokens * 2 * layers * kvHeads * headDim * kvBytesPerValue;
const gib = bytes => bytes / (1024 ** 3);
const estimatedGiBPerGpu = gib(weightsPerGpu + kvBytes) + runtimeHeadroomGiB;
console.log({ weightsGiBPerGpu: gib(weightsPerGpu), kvGiB: gib(kvBytes), estimatedGiBPerGpu });The output separates estimated weights per GPU and KV cache from the combined estimate. In this example, the chosen 2 GiB headroom is an input assumption, not a generally validated allowance. For multiple GPUs, the weight estimate divides weights evenly by the tensor-parallel GPU count; actual sharding and runtime allocations may not divide that neatly. The KV figure is a workload-wide calculation in this simplified example, not a promise that each GPU holds that amount or that it is evenly distributed.
Choose inputs that match the model and workload
Weights and GPU count
Use total parameter count and an effective bytes-per-weight value that reflects the stored format. NVIDIA’s heuristic for per-GPU weights is parameters multiplied by bytes per parameter, divided by the tensor-parallel GPU count. That division is a rough allocation model; confirm how the selected engine shards weights and what VRAM is usable on each GPU. Keep per-GPU capacity distinct from total memory across the cluster.
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As a scale reference, NVIDIA’s technical blog estimates about 14 GB of FP16 weights for a 7-billion-parameter model. The figure illustrates the arithmetic; actual quantized model storage and runtime placement can differ. The blog also gives an illustrative 24 GB RTX 4090 example, not a blanket GPU recommendation. Read NVIDIA’s explanation of LLM inference memory.
KV cache dimensions
For a common transformer cache, the approximate bytes per token per sequence are:
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2 × layers × KV heads × head dimension × bytes per cache value
The factor of two accounts for keys and values. Multiply by the number of sequences and the total tokens retained in each sequence to estimate the workload’s cache. NVIDIA presents a broad form of this calculation using hidden size; where attention uses grouped-query attention, use the number of KV heads rather than the number of query heads. Check the model configuration for layer count, KV-head count, and head dimension instead of assuming these values from a model family name.
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NVIDIA’s Llama 2 7B example estimates roughly 2 GB of half-precision KV cache at batch 1 and sequence length 4096 under its stated architecture assumptions. It is an example, not a universal cache size. See the NVIDIA Technical Blog’s KV-cache discussion.
Tokens, batch, and context
cachedTokens should represent the total tokens retained at the point you are sizing, not only the prompt. If the runtime retains the prompt plus generated output, include both in the token budget. Increasing context length or concurrent sequences increases KV allocation; reducing weight precision changes weight storage instead. Some inference engines also expose memory budgets that influence how much cache they allocate. NVIDIA discusses cache sizing and memory controls in its NIM performance documentation.
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Interpret the result before choosing hardware
Compare the estimate with usable VRAM per GPU, not just the combined advertised capacity of a multi-GPU system. A setup may have enough aggregate memory but still fail if weights or cache are not placed across devices as assumed. Also compare options using the same model, precision, context, and concurrency; changing any of those can materially alter the capacity estimate.
- Check whether the target model’s weight format and KV-cache representation match the byte assumptions.
- Confirm the runtime’s tensor-parallel layout and per-GPU memory use.
- Reserve memory for runtime allocations based on the engine and workload; the script’s headroom value is your assumption.
- Validate with the actual model and serving configuration, because peak use can diverge from this simplified calculation.
This is a capacity estimate only. It does not rank GPUs by performance or establish runtime compatibility; those depend on the hardware, software stack, and workload.
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