How Much VRAM Do You Need to Run Local LLMs? 2026 Benchmark Data

The barrier to running large language models (LLMs) locally is changing fast. When you see claims like "an 8GB GPU can run a 7B model," there's actually a not-so-simple calculation hiding behind it. VRAM requirements aren't a fixed number—they're determined by model weight quantization, KV cache size, and context length working together.

The Two Core VRAM Consumers: Weights and KV Cache

Weights: Q4 Quantization Is About 30% of BF16

Model weights are the foundation of VRAM usage. Stored at BF16 precision, each parameter needs 2 bytes. With Q4 quantization (4-bit), each parameter averages only about 0.5 bytes—the article notes that "Q4 weights are roughly 30% of BF16," a ratio that accounts for both quantization scaling factors and possible redundant storage.

For example, a 7B-parameter model:

• BF16 precision: about 14 GB
• Q4 quantization: about 4.2 GB

This means quantization is the most direct way to lower the barrier to local deployment. But weights are only the starting point—the real variable comes later.

Quantization scaling factors exist to avoid numerical overflow or precision loss from low-precision representation. With 4-bit quantization, for instance, each weight is stored in just 4 bits, giving a very limited representable integer range (e.g., 0-15). To fit the weight distribution to this range, a scaling factor and zero-point offset must be stored for each quantization group (typically every 32 or 128 weights), and these extra parameters consume a small amount of VRAM. In addition, some quantization schemes retain a small number of high-precision weights (such as outlier channels) or use group quantization that adds metadata overhead, so actual usage is slightly higher than the theoretical 0.5 bytes/parameter. The fact that Q4 weights are about 30% of BF16 (rather than the purely theoretical 25%) reflects these additional overheads.

The reason the KV cache grows with every token is that during autoregressive generation, the model must cache the key (Key) and value (Value) tensors of historical tokens to avoid recomputation. Each time a new token is generated, a new pair of K/V vectors is appended for every layer, so the cache size is proportional to the number of tokens processed. The longer the context, the more cache entries, and VRAM usage naturally rises linearly. This is also the root cause of OOM errors during long conversations.

KV Cache: Linear Growth with Context Length

The KV cache is the key-value pairs saved for each token during inference, used to speed up attention computation. Its size depends on the number of layers, hidden dimensions, number of attention heads, and context length. The article emphasizes that "the KV cache grows with every token"—the longer the context, the larger this overhead.

For long-context scenarios (e.g., 32K tokens), the KV cache can reach several GB or even a dozen-plus GB, easily exceeding the size of the 4-bit weights themselves. This is why many people suddenly hit out-of-memory (OOM) errors during long conversations.

Real Data from 8GB to 256GB

The article provides measured ranges from 8GB to 256GB of VRAM, covering the full spectrum from consumer GPUs to workstation/multi-GPU configurations.

8GB VRAM: What Can It Run?

8GB is a common capacity for entry-level GPUs (such as the RTX 3060 Ti or RTX 4060). At this level, a Q4-quantized 7B model (about 4.2GB of weights) can run, but the space left for the KV cache is limited. If the context is kept within 4K tokens, the experience is acceptable; once it stretches beyond 8K, you'll easily hit the VRAM ceiling.

Smaller models (such as 3B or 1.5B) can handle long contexts more comfortably at 8GB, but inference quality will drop somewhat.

16-24GB: The Mainstream Mid-Range

16GB (such as the RTX 4060 Ti 16G or RTX 4070 Ti Super) and 24GB (such as the RTX 3090 or RTX 4090) are the main range for local LLM enthusiasts. At this capacity, a Q4-quantized 13B model (about 7-8GB of weights) can run with 8K-16K context. 24GB can further support 30B-class Q4 models, or a 13B model with longer context.

48GB and Above: Large Models and Long Context

48GB (such as the RTX 6000 Ada or dual 24GB cards) begins to enter the threshold for Q4-quantized 70B models—70B Q4 weights are roughly in the 35-40GB range, with the remaining space used for KV cache and inference intermediate activations.

256GB is the domain of workstation-class setups (such as multi-GPU A100/H100 or a Mac Studio with massive unified memory), capable of running unquantized medium models or quantized ultra-large-scale models. The data range in the article shows that VRAM requirements don't grow linearly—each time you cross a model-scale threshold, the required VRAM jumps up a step.

Hidden Variables Affecting VRAM Requirements

Context Length Is the Biggest Adjustment Knob

Many people focus only on model parameter count while ignoring the amplification effect of context length on the KV cache. For the same model, VRAM usage at 4K context versus 32K context can differ severalfold. The article specifically points out that "the KV cache grows with every token" as a reminder of this.

Inference intermediate activations are tensors temporarily produced during the forward computation, including attention scores and feed-forward network intermediate results. Unlike weights and the KV cache, the size of activations depends mainly on batch size and sequence length, not model parameter count. In local inference scenarios with a batch size of 1, activation overhead is usually small, but with extremely long contexts or when batching multiple requests, activations can reach several GB. In addition, some inference frameworks reduce activation peaks through memory reuse or chunked computation, but this also increases the complexity of VRAM management.

Unified memory architectures (such as Apple Silicon) share the same physical memory between CPU and GPU, so the "VRAM" limit is actually determined by total system memory rather than the dedicated VRAM of a discrete GPU. This means a Mac Studio with 256GB of unified memory can allocate far more than 24GB to the GPU for model inference. However, the bandwidth of unified memory is typically lower than the VRAM bandwidth of high-end discrete GPUs (such as HBM), and the GPU and CPU contend for the same memory pool, so actual inference speed may not match a discrete GPU of the same capacity. This architecture offers another path to scaling up for local large models, but bandwidth and capacity must be weighed.

Quantization Precision and Inference Frameworks

Q4 is just a common choice; Q5 and Q8 weight sizes increase in turn. In addition, different inference frameworks (llama.cpp, vLLM, ExLlama, etc.) have different KV cache management strategies, so actual usage will vary. The article's 30% ratio is a representative estimation baseline, but specific deployments should still be based on actual measurements.

The Special Case of Unified Memory Architecture

On devices with unified memory architectures such as Apple Silicon (e.g., Mac Studio), "VRAM" is shared with system memory, and 256GB of unified memory can allocate far more to the GPU than traditional discrete GPUs. This offers another path to scaling up for local large models, but the bandwidth characteristics differ from discrete GPUs, so inference speed will vary.

How to Estimate Your Own VRAM Requirements

A rough formula is: VRAM requirement ≈ weight size + KV cache + inference intermediate activation overhead.

Weights: parameter count × bytes per parameter (about 0.5 for Q4, 2 for BF16).

KV cache: 2 × number of layers × hidden dimension × context length × bytes per element (usually 2 bytes for FP16). Specific values vary by model architecture.

In practice, it's recommended to first load the model with an inference framework and monitor VRAM usage, then gradually increase context length to find the stable boundary on your hardware. The data points from 8GB to 256GB in the article can serve as a reference to help you judge whether your target model falls within your hardware's range.

There's no one-size-fits-all answer to VRAM planning for local LLMs. Quantization compresses weights to about 30% of BF16, but the KV cache keeps expanding with the context you feed the model. Understanding how these two parts interact is more valuable than memorizing any specific number.