To choose an open model for one GPU, estimate whether its weights, KV cache and serving runtime fit in the VRAM available for your intended context length and number of simultaneous requests. Parameter count alone cannot tell you that. The same model size can have different memory needs depending on weight precision, quantization, runtime and workload, so there is no reliable universal rule that a certain number of billions will fit in a certain amount of VRAM.
Why parameter count does not predict whether a model fits
Parameter count describes the number of learned values in a model, not the complete memory bill for running it. The weights are only one allocation: inference also needs memory for the KV cache, while the serving runtime and other processes consume additional GPU memory.
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The vLLM authors’ 2023 deployment table makes the distinction explicit. For one 13B configuration, it reports 26 GB of parameter memory plus 12 GB for the KV cache on one A100 with 40 GB total GPU memory. Those figures describe that paper’s configuration, not every 13B model or current engine. Larger examples also show how the cache can be substantial: the paper reports 132 GB of parameter memory and 21 GB of KV-cache memory for a 66B configuration on four A100 GPUs, and 346 GB plus 264 GB for a 175B configuration on eight A100-80GB GPUs. vLLM paper.
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These historical examples illustrate the accounting, not a sizing calculator for a GPU you own. A useful fit estimate must specify the model representation, inference engine, context length, active requests and available VRAM.
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What consumes GPU memory during inference
Model weights and their precision
Weight storage depends on how the model is represented. Quantization can reduce weight memory, but the parameter count by itself does not establish the actual footprint. Record the specific checkpoint and weight format you plan to run rather than estimating from a headline model size.
KV cache, context and concurrency
The KV cache stores information used to generate tokens as requests progress. Longer contexts and more active sequences increase cache demand, leaving less VRAM for other allocations. In vLLM, insufficient KV-cache space can constrain the workload; its documentation recommends reducing the number of sequences or batched tokens when cache space is insufficient. vLLM optimization and tuning documentation.
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Runtime allocation and other GPU users
The inference engine needs memory too, and the amount available to the model can be lower than the GPU’s advertised VRAM if other applications are using the card. vLLM’s GPU memory utilization setting controls preallocated cache, so a memory estimate should account for the selected engine and its configuration rather than treating all VRAM as free for weights. vLLM optimization and tuning documentation.
How to compare candidates for your GPU
- Check usable VRAM. Identify your GPU and the memory actually available to inference while your usual applications are running.
- Record each candidate’s representation. Note the exact model checkpoint, weight precision or quantization format, and any relevant runtime settings. Do not derive the footprint from parameter count alone.
- Set the workload you need to support. Choose the target context length and number of simultaneous requests. Estimate or inspect KV-cache use at those settings; for vLLM, review the startup memory profile and cache allocation for your version and configuration.
- Check compatibility. Confirm that the chosen engine supports the model architecture, quantization format and GPU. vLLM lists a range of quantization formats, but availability depends on version and hardware. vLLM quantization documentation.
- Test the exact setup. Run the intended workload on the GPU and engine you plan to use. Compare whether it fits, the context and concurrency headroom, and measured latency or throughput.
- Judge output quality for your task. A configuration that fits is not automatically the right choice if its output quality does not meet your needs. Memory and serving measurements alone do not establish which model is best for a particular task.
When one GPU is not enough
If a model or workload does not fit, first consider whether a different weight quantization, lower concurrency or shorter context meets your requirements. You can also try a smaller model. Each change affects a different part of the problem, so test the revised configuration rather than assuming a parameter-count threshold guarantees success.
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If you need the same model and workload, an inference setup that spreads the model across GPUs may be an option. vLLM’s optimization guidance says tensor parallelism is essential for models too large to fit on one GPU, citing 70B models as an example. This is guidance about vLLM’s parallel-deployment strategy, not a claim that every 70B model fails on every single GPU: memory use varies with representation and workload. vLLM optimization and tuning documentation.
Consider a graphics card with more VRAM only after defining the model, format, context and concurrency you need. Without those inputs, neither a specific upgrade nor a personalized fit threshold can be justified.
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What quantization can—and cannot—tell you
Weight quantization and KV-cache quantization are separate choices. Reducing precision can lower memory demand, but compatibility, behavior and speed depend on the model, GPU and runtime. Confirm support for the exact combination and measure it; a smaller memory footprint is not, on its own, proof of better performance or acceptable output quality. vLLM quantization documentation.
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A vLLM Project benchmark reports that FP8 KV cache produced 54% of the BF16 inter-token-latency slope for Llama-3.1-8B on a single H100 using vLLM v0.19.1. That is a result under the project’s specific benchmark conditions, not a general performance promise for other GPUs, models or workloads. vLLM v0.19.1 quantization documentation.
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A practical comparison checklist
- Available VRAM after accounting for other GPU users.
- Weight footprint for the exact checkpoint and precision.
- KV-cache demand at your target context length and concurrency.
- Runtime overhead, cache allocation and compatibility.
- Whether the configuration fits with useful headroom.
- Measured latency or throughput on your actual GPU and engine.
- Output quality for the task you need the model to perform.
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