Monsder guide
How much VRAM does an LLM need?
Running an LLM requires memory for model weights, the KV cache, working buffers and runtime overhead. Parameter count alone is insufficient: specify the exact model version, weight format, context length and concurrent requests.
How to estimate weight memory
A simplified formula is: weight memory in bytes ≈ parameter count × bits per parameter / 8. For a hypothetical model with exactly 8 billion parameters, FP16 gives 16 GB, or about 14.90 GiB; idealized 4-bit weights give 4 GB, or about 3.73 GiB. GB = 10⁹ bytes; GiB = 2³⁰ bytes.
This is an arithmetic example, not the requirement of a specific 8B model. A real quantization format adds metadata and may keep some tensors at another precision. A 3.73 GiB weight estimate does not establish that the model will run on a 4 GiB GPU.
Why context and concurrency matter
The KV cache stores intermediate attention keys and values. Its size depends on architecture, cache precision, retained tokens and active sequences. Long context and concurrent conversations can substantially increase memory use; sliding-window attention and other architectures behave differently.
Employee count is not the number of requests processed simultaneously. Specify active concurrency and expected input and output lengths. If only daily user count is known, state the assumption and determine peak load.
How to read a Monsder estimate
Choose a model and workload mode in the calculator, then set precision, context and concurrency. The result shows estimated requirements and known limitations. An incomplete memory profile remains a lower bound: unknown memory is never converted to zero.
Fitting in VRAM does not verify throughput, latency or reliability. Next, validate the chosen runtime and measure the target hardware. Training also needs gradients, optimizer states and activations; an inference estimate cannot be reused for training without recalculation.
Common questions
Is 24 GB enough for an LLM?
It depends on the model, weight format, context and concurrency. Even if weights fit, the KV cache and runtime may need additional memory. Enter these parameters in the calculator and review the assumptions.
Does 4-bit quantization reduce all memory by four?
No. The ideal 4-to-16 ratio applies to weight representation. The KV cache, buffers and metadata have their own formats and memory costs.
Sources and scope
Primary documentation explains memory and runtime principles. The Monsder workflow describes the current preliminary calculator. Examples are not GPU benchmarks; final engineering decisions require specialist review.
Apply the method
- How to choose an AI server
Choose an AI server from workload requirements: model, load, GPU, memory, power and cooling. What a calculator estimates and what needs engineering validation.
- Which models fit your GPU?
Match an LLM to your GPU using available VRAM, quantization, context and concurrency. Why fitting in memory does not establish a validated deployment.