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To estimate whether a local language model will fit on your GPU, start with its weight memory, then budget space for the key-value (KV) cache, runtime overhead and whatever your desktop is already using. A useful first calculation is parameter count × bytes per parameter ÷ tensor-parallel GPUs. In BF16 or FP16, that is roughly 2 GB per billion parameters; FP8 uses about 1 GB per billion and INT4/NVFP4 about 0.5 GB per billion. Those figures are only the starting point. Context length, concurrent requests, backend reservations and CPU offload can determine whether an otherwise suitable model actually runs. This guide shows how to make the estimate, apply it to 7B, 8B, 13B and 70B models, and diagnose out-of-memory errors.
What does GPU memory need to hold during local LLM inference?
Inference memory is not just the downloaded model file. A running server generally needs room for four things:
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- KV cache: attention keys and values retained for each token in the prompt and each generated token.
- Runtime allocations: activations, CUDA context, communication buffers, CUDA graphs, temporary workspaces and adapter weights.
- Headroom: unreserved capacity for allocator fragmentation, changing request sizes and other programs using the card.
The model fits only when the sum of those allocations is below free VRAM at runtime. A card marketed as having 24 GB may offer less to the inference process after a display server, browser, game launcher or monitoring tool has claimed memory.
Memory use also changes while the server runs. A short one-off prompt can use far less KV cache than a long chat, and a single request uses less cache than several users generating simultaneously. Treat capacity as a range tied to a context limit and concurrency target, not as a permanent property of the model name.
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How do I calculate model weight memory?
Use this estimate before looking at any GPU specification:
weight memory per GPU = total parameters × bytes per parameter ÷ tensor-parallel GPUs
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For a rough GB result, treat “billion parameters × bytes per parameter” as gigabytes. The estimate follows the precision sizes documented by NVIDIA, while Hugging Face describes the BF16/FP16 shortcut as roughly 2 × the parameter count in GB.
| Weight format | Approximate bytes per parameter | Quick estimate |
|---|---|---|
| BF16 or FP16 | 2 bytes | 2 GB per billion parameters |
| FP8 | 1 byte | 1 GB per billion parameters |
| INT4 or NVFP4 | 0.5 byte | 0.5 GB per billion parameters |
These are planning values, not the exact size of every file. Quantized formats can leave selected tensors at a higher precision, and metadata, scales and alignment add overhead. A 4-bit file therefore may be larger than exactly half the BF16 estimate. Use the model’s documented size when available, then add the other allocations described below.
What does tensor parallelism change?
Tensor parallelism (TP) splits weight tensors across GPUs. With two-way TP, the idealized weight allocation per GPU is half the total; with four-way TP, it is one quarter. Communication buffers and synchronization still consume memory, so the result is not a promise that each card can use its full theoretical share.
For example, NVIDIA gives the same relationship as weight_memory_per_gpu = total_parameters × bytes_per_parameter / TP. TP can make a model fit, but it also requires a backend that supports the selected GPU arrangement and a fast interconnect if you want good throughput.
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How much VRAM do 7B, 8B, 13B and 70B models need?
The following values are weight estimates before KV cache, runtime allocations and headroom. The named measurements come from current Hugging Face and NVIDIA documentation.
| Model example | Precision or condition | Weight memory | What the figure means |
|---|---|---|---|
| Mistral 7B | BF16 | 13.74 GB | Documented loaded-weight measurement |
| Mistral 7B | 8-bit | 6.87 GB | Documented loaded-weight measurement |
| Llama 3.1 8B | BF16 | About 16 GB | NVIDIA says it can fit on a 24 GB RTX 4090 with room for KV cache and overhead |
| Llama 2 70B | BF16 | Approximately 140 GB | Hugging Face estimate for weights alone |
| Llama 3 70B | 128K context, one user | About 40 GB of KV cache | NVIDIA Developer estimate for cache in addition to the weights |
For models without a published loaded size, multiply the parameter count by the bytes-per-parameter value. Thus a 13B BF16 model is about 26 GB of weights before anything else, which normally exceeds a 24 GB card. A 70B BF16 model is roughly 140 GB of weights, so a single consumer GPU cannot hold it at that precision; even four-way TP would leave about 35 GB of weights per GPU before overhead.
Why does context length affect VRAM so much?
The KV cache stores attention keys and values for the tokens a model must remember. It grows with the number of prompt and generated tokens, the model’s layer and head dimensions, and the number of simultaneous sequences. Increasing the maximum context therefore reserves memory even when the current prompt is short, depending on the backend’s allocation strategy.
A long-context configuration can consume tens of gigabytes after the weights already fit. NVIDIA’s 2025 estimate puts the KV cache for one user of Llama 3 70B at 128K context at about 40 GB. That number is model- and implementation-specific; it should not be applied to every 70B model or every context length.
Which context and concurrency settings should I budget?
- Maximum context: size for the largest prompt-plus-generation length you will permit, not the average chat.
- Typical context: measure the memory at the length your application normally reaches; a lower cap can make a practical difference.
- Concurrency: assume each active sequence needs its own cache. Batch size and continuous batching can increase the same allocation.
- KV-cache precision: some backends support a quantized cache. It can reduce memory, but verify supported data types and any quality or speed trade-off in that backend.
If a server fails while reserving or profiling the cache, reduce the maximum context or concurrent sequence count before changing the model weights.
How much headroom should I leave?
After weights and KV cache, reserve space for activations, the CUDA context, temporary kernels, communication buffers, CUDA graph capture and adapter modules. Allocator fragmentation can make an allocation fail even when the arithmetic sum appears to fit.
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NVIDIA’s NIM example uses a gpu-memory-utilization default of 0.9, budgeting 90% of total GPU memory for model operations and leaving 10% unreserved. That is a backend-specific heuristic, not a universal safety margin. Other runtimes may reserve differently, so inspect their logs and configuration.
As a practical rule, do not plan a sustained workload at the card’s advertised maximum. Leave additional room when the GPU also drives a display, records video, runs a desktop compositor or shares work with another process. The relevant baseline is the peak free VRAM immediately before the model starts.
How can I check whether a model will fit before downloading or launching it?
Use this repeatable sizing process:
- Record the model size and format. Confirm the parameter count and whether the checkpoint is BF16, FP16, FP8, 8-bit, 4-bit or another format. If the publisher provides a measured loaded size, prefer it over a simple multiplication.
- Calculate weight memory. Multiply parameters by the bytes-per-parameter estimate. Divide by the number of GPUs only when your inference stack will actually use tensor parallelism.
- Set a context and concurrency target. Write down the maximum prompt-plus-output tokens and the number of simultaneous sequences. Do not use a benchmark’s cache figure without matching those conditions.
- Estimate or measure KV cache. Check the backend’s model-specific calculator or startup log. If no calculator exists, start with a conservative context limit and increase it while watching VRAM.
- Subtract existing usage. Run
nvidia-smior your platform’s equivalent immediately before launch and use free memory, not total capacity. Include the display server and any other GPU processes. - Keep headroom. Apply the backend’s memory-utilization setting and leave extra space for graph capture, fragmentation and bursty requests.
- Perform a representative warm-up. Send a prompt at the intended context length and concurrency. Some allocations occur only during warm-up or CUDA graph capture, so a successful model load alone is not proof of capacity.
The result should be a tested operating envelope: model format, context cap, concurrency, number of GPUs and observed peak memory. Record those settings so a later model update or desktop workload does not silently invalidate the estimate.
What happens with common local-inference scenarios?
8B BF16 on a 24 GB card
An 8B BF16 checkpoint needs about 16 GB for weights. NVIDIA’s Llama 3.1 8B example shows that this class can run on a 24 GB RTX 4090 while retaining room for KV cache and overhead. The remaining capacity is not a fixed “8 GB of usable cache”: desktop usage, the chosen context limit, backend reservations and concurrent requests determine the actual margin.
13B BF16 on a 24 GB card
At approximately 26 GB of weights, a 13B BF16 model normally exceeds a 24 GB card before KV cache is allocated. Choose a lower-precision or quantized checkpoint, use multiple GPUs with supported TP, or offload part of the model to system RAM. Offloading can make loading possible but usually reduces generation speed because data must cross the bus.
70B BF16 across multiple GPUs
A 70B BF16 model requires approximately 140 GB for weights. Four-way TP reduces the idealized weight share to about 35 GB per GPU, still leaving no room on common 24 GB cards. More GPUs, lower precision or a combination of TP and offload is required, and the KV cache must be budgeted separately for every active sequence.
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Quantized checkpoints
For a quantized model, multiply the parameter count by the format’s approximate bytes per parameter, then add cache and overhead exactly as you would for BF16. A nominal 4-bit format uses about 0.5 byte per parameter, but actual GGUF and similar files can keep some tensors at higher precision. Compare the file’s real size and the backend’s startup report rather than assuming a perfect half-size reduction.
Should I use quantization, CPU offload or more GPUs?
Choose the remedy that addresses the allocation that failed:
- Lower precision or quantization: reduces weight memory and is usually the simplest way to move a model onto a smaller card. Quality and speed vary by model, quantizer and workload, so validate the outputs that matter to you.
- More GPUs with tensor parallelism: divides weights and can preserve higher precision. It adds communication overhead and requires compatible software, device topology and enough aggregate memory for cache and runtime buffers.
- CPU or system-RAM offload: allows a model to exceed VRAM, but transfers over PCIe or another link can sharply lower tokens per second. It is more suitable when capacity matters more than latency.
- Lower context or concurrency: targets KV-cache pressure without changing model quality. This is often the right fix for a cache-allocation failure.
Memory capacity and inference performance are separate decisions. Two 24 GB cards can hold similarly sized checkpoints yet deliver different throughput because of memory bandwidth, compute capability, interconnect and backend support.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I diagnose an out-of-memory error?
Match the timing of the failure to the allocation that caused it:
| Failure point | Likely cause | First corrective action |
|---|---|---|
| During weight loading | Weights exceed free VRAM at the selected precision | Use lower precision, a smaller model, more GPUs or a higher TP degree |
| During KV-cache allocation or memory profiling | Context limit or concurrency requires too much cache | Lower maximum context, lower concurrency or enable a supported quantized KV cache |
| During CUDA graph capture or warm-up | Temporary allocations, graph memory or fragmentation consume the remaining margin | Leave more unreserved memory and inspect backend logs and utilization settings |
| Only when the desktop or another job is active | Shared graphics workload removes the assumed free VRAM | Subtract that workload’s peak usage or move inference to an otherwise idle GPU |
After each change, restart the process and recheck free memory. GPU allocators may retain reservations until the process exits, so testing successive settings in the same process can give misleading results.
How should I compare GPUs for local LLM inference?
Compare a complete configuration rather than VRAM labels alone. The important axes are:
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- usable free VRAM after the operating system and other applications;
- model parameter count and weight precision;
- maximum and typical context length;
- number of concurrent users or target batch size;
- tensor-parallel degree, CPU offload and interconnect;
- backend-specific memory reservations and supported cache formats; and
- generation speed after any offloading.
A 24GB VRAM graphics card is a useful sizing anchor for small and medium local models, and NVIDIA’s RTX 4090 example demonstrates why that capacity is relevant. However, current prices, availability and seller quality change, and equal capacity does not mean equal inference performance. Check the exact card, backend support and measured free memory before buying for a particular model.
What is the safest way to plan capacity for a production-like local server?
Define the workload first: model revision, precision, context ceiling, average and peak prompt lengths, concurrent sequences and acceptable latency. Then test a worst-case prompt at the planned concurrency, including the desktop or monitoring agents that will remain enabled. Capture peak VRAM during loading, warm-up and generation, and keep an operational margin so a small model update or traffic spike does not trigger an out-of-memory restart.
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Frequently Asked Questions
Can system RAM replace VRAM for a local LLM?
Many runtimes can offload weights or cache to system RAM, but the model then moves data across PCIe or another host link. This can prevent a capacity failure while substantially lowering tokens per second, so treat offload as a fallback rather than equivalent GPU memory.
Does quantization always reduce answer quality?
No single outcome applies to every model. The effect depends on the quantization method, calibration data, layer choices and task; compare the exact checkpoint on representative prompts instead of assuming that all 4-bit or 8-bit variants behave alike.
Why does the same model use different VRAM in different applications?
Backends make different choices for KV-cache layout, temporary workspaces, CUDA graphs, batching and memory reservations. Their defaults can also set different context limits, so compare startup logs and peak usage under identical settings rather than comparing file sizes alone.
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Measure the desktop’s peak, not just its idle usage, while opening the applications you normally keep running. Reserve that amount before sizing the model and cap context or concurrency so interactive graphics still have breathing room.
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