Discover how to calculate memory requirements for local LLMs and run language models on even low-end PCs. We’ll cover all factors that affect RAM usage – model size, KV cache, precision, batch size, etc. – and show how to compute model weight and cache usage. Along the way, we’ll share practical examples (Ollama, LM Studio, Kolosal.ai) and give optimization tips for CPU-only LLM deployment. By the end, you’ll know exactly how much RAM you need to run different LLMs locally, and how to optimize for your hardware.
Local LLMs are becoming important because they offer privacy, offline use, and no cloud fees. However, running a model on-device requires careful planning of memory requirements for local LLMs – especially on low-end devices with limited RAM. The total memory needed is dominated by the model weights and the key-value (KV) cache during inference. In this guide, we’ll explain these factors in depth and show how to calculate them. We also introduce Kolosal.ai, an open-source local LLM platform, which simplifies running models on your machine.
Factors Affecting Memory Requirements
Several key factors determine how much RAM a local LLM will consume:
- Model size (parameters and precision): The largest component is the model weights. More parameters = more memory. For example, an LLM with 7 billion parameters in 16‑bit precision requires roughly 14–15 GB of memory (7B × 2 bytes/parameter). A 13B model at 16-bit needs about 26 GB, and a 33B model ~66 GB. Using lower-precision (e.g. INT8 or INT4) cuts this dramatically (7B in 8-bit is ~7 GB). Inference also needs a small overhead (~10–20% more) for activations, but weights dominate.
- KV cache (context length and layers): During generation, the model caches past “key” and “value” vectors for each layer. The cache grows linearly with context length. Memory = 2 × (num_layers) × (num_heads) × (seq_len) × (head_dim) × (bytes per value). For example, a Llama-2 7B (32 layers, 32 heads, 128 head-dim) with 4,096-token context uses ~2 GB of KV cache. (Halving context halves KV memory.) Longer contexts (e.g. 32K tokens) multiply memory accordingly.
- Batch size: More parallel sequences multiply the KV cache size. For a single sequence (batch=1), use the above formula. Larger batches mean proportionally more KV storage and also more activation memory.
- Other factors: Concurrent users or beams increase memory linearly. Training-specific memory (optimizer state, gradients) is far larger, but for inference on CPU we focus on weights and cache.
How to Calculate Memory Usage
Model Weight Memory
To estimate model weight size (in GB): multiply parameter count by bytes per parameter. For inference on CPU, BF16 (2 bytes) or FP16 is common. For example, a 7B-BF16 model uses about 14 GB (7e9×2 bytes). In practice, it’s simpler to approximate “#params × 2” (GB for BF16) or “×1” (GB for int8) as a rule of thumb. You should also account for an overhead (often ~10–20%) for activations or miscellaneous buffers.
KV Cache Memory
During autoregressive generation, each new token adds its key/value vectors to the cache. The formula for KV memory (bytes) is:
KV_bytes = batch_size × seq_len × (2 × num_heads × head_dim) × bytes_per_element
The factor of 2 is for both Key and Value tensors per token. For FP16 (2 bytes), this becomes:
KV_GB ≈ batch_size × seq_len × num_layers × num_heads × head_dim × 4 / 2^30
As a concrete example, Llama-2 7B (32 layers, 32 heads, head_dim=128) with a batch of 1 and context of 4096 tokens has:
1 × 4096 × 32 × 32 × 128 × 2 bytes ≈ 2×10^9 bytes ≈ 2 GB.
In other words, each token adds about 0.5 MB for K/V. So 2048 tokens ≈1 GB. More generally, doubling context doubles KV usage (e.g. 2048→4096 tokens doubles the cache).
1 × 4096 × 32 × 32 × 128 × 2 bytes ≈ 2×10^9 bytes ≈ 2 GB.
In other words, each token adds about 0.5 MB for K/V. So 2048 tokens ≈1 GB. More generally, doubling context doubles KV usage (e.g. 2048→4096 tokens doubles the cache).
shows NVIDIA’s example: a 7B model (4096 dims) with 4096 tokens yields ~2 GB KV in 16-bit. Kolosal’s analysis also notes KV memory grows with layers and heads: for a 35B model (40 layers, 64 heads, 128-dim) it needed ~2.56 GB for 2048 tokens.
Practical Examples
- Ollama: Their docs state ~16 GB RAM for running models up to 7B parameters. (They recommend an 8-core CPU for up to 13B.) In practice, Ollama users find a 7B model comfortable on 16–32 GB, while a 13B model often needs ≥32 GB.
- LM Studio: The LM Studio system requirements advise 16 GB+ RAM for local models. (It notes even on an 8 GB Mac you can run small models with modest context.) They explicitly say 16+ GB for standard use, aligning with the rule that 4–7B models need ~16–32 GB.
- Kolosal.ai: As an example of a local LLM platform, Kolosal AI is designed for lightweight on-device inference. Its guides highlight CPU-friendly models and memory footprints. For instance, Kolosal’s “Top 5 CPU LLMs” article notes 1.5B–3B models run on ~16 GB RAM. Kolosal provides an easy GUI to load these models on your PC.
These examples show the pattern: small models (1–3B) run in ~8–16 GB, medium models (4–7B) in ~16–32 GB, and anything above (13B+) starts needing 32–64+ GB. We’ll summarize these in the table below.
Typical Memory Needs by Model Size
The table below shows approximate requirements for common model sizes (weights + needed headroom):
| Model Size | Weight (FP16/BF16) | KV Cache (2048 tokens) | Total RAM (approx.) |
|---|---|---|---|
| 7B | ~14 GB | ~1 GB | ~16–32 GB |
| 13B | ~26 GB | ~1.6 GB | ~32–64 GB (often ≥32 GB) |
| 33B | ~66 GB | ~3 GB | ≥66 GB (recommend ~80+ GB) |
These are rough guidelines. For 7B models, the ~14 GB weight means you should allow 16–32 GB of system RAM. A 13B FP16 model (~26 GB weight) typically needs at least 32 GB of RAM, often more if context is long. A 33B model’s FP16 weights alone are ~66 GB, so realistically one would use 80+ GB RAM or split it across devices. In practice, quantized versions (INT8/4) dramatically lower these needs.
Tips for Optimizing Memory Use on CPU-Only Devices
- Use quantization: Convert weights to 8-bit or 4-bit formats. Quantizing a model can cut its RAM use by ~2–4×. For example, a KV cache that is 64 GB in FP16 becomes only ~16 GB in 4-bit. Quantized weights likewise shrink model size (7B int8 ~7 GB). Many frameworks (like llama.cpp or bitsandbytes) support int8/4 inference.
- Limit context length: Keep
max_tokensmodest. Reducing sequence length has a linear impact on KV cache memory. For example, halving the context from 4096 to 2048 tokens roughly halves the cache. If you only need short prompts or chat, cap it accordingly. - Batch size = 1: Processing one input at a time minimizes concurrent KV use. Each extra batch sequence multiplies memory needs. If throughput isn’t critical, use batch size 1.
- Flash/Grouped Attention: Some libraries implement memory-efficient attention (e.g. FlashAttention, Grouped Query Attention) that reduce KV memory spikes. These are often built into inference engines (for instance, setting environment flags or using specialized kernels).
- Pick smaller models: If RAM is limited, opt for distilled or smaller-size variants. As Kolosal notes, 1–3B models often hit the sweet spot on CPU. For example, Gemma 1B or DeepSeek 1.5B run fast on ~8–16 GB.
- Offload if needed: Some tools (like llama.cpp) can memory-map model files from disk instead of loading all into RAM. This can allow running large models with swap, albeit slower. It’s a last resort but can save RAM at cost of speed.
- Use efficient frameworks: Kolosal.ai or LLama.cpp, Ollama, LM Studio, etc., handle many low-level optimizations for you. Using these platforms avoids manual tinkering and ensures models load only needed parts.
Using lower-precision quantization (8-bit, 4-bit) and efficient attention kernels (FlashAttention) can dramatically cut memory usage for local LLMs.
FAQ
-
How much RAM do I need to run a 7B model locally?
Roughly 16–32 GB. A 7B model’s FP16 weights are ~14 GB, and a modest context adds ~1 GB of KV cache. In practice, allocate a bit extra (OS, inference process, etc). Users report that 16 GB can just run 7B, but 24–32 GB ensures smooth operation. -
What is KV cache in LLMs?
KV cache (Key-Value cache) stores the past attention keys and values for all input tokens. It speeds up generation by reusing these instead of recomputing. The cache grows as you generate more tokens. Its memory is roughly2 × layers × heads × sequence_length × head_dim × bytes. In simple terms, more context → more KV memory. -
Can I run local LLMs without a GPU?
Yes. Many modern models are optimized for CPU inference. For example, Llama.cpp or Kolosal AI can run 7B (even 13B) models on a high-end CPU with enough RAM. It’s slower than GPU, but entirely possible: “if you have 32GB RAM and lots of cores you can run 13B,” one user notes. In practice, very small models (<3B) run comfortably under 16 GB. -
How does sequence length affect memory usage?
Almost linearly. Longer input/contexts increase KV cache memory linearly. For example, doubling max tokens from 2K to 4K doubles the cache size. So a longer context can dramatically raise RAM needs. If you have tight memory, reducingmax_lengthor trimming prompts helps manage usage. -
What tools help estimate LLM memory requirements?
Tools like Hugging Face’s Accelerate includeaccelerate estimate-memory, which predicts model memory use by type. There are also community calculators (e.g. peft/transformers tools) that compute weight + KV memory for given context. Kolosal.ai and LM Studio guides also give rough requirements for models. In short, use a memory estimator or start with “params × bytes” and add context cache.
Conclusion
Estimating memory requirements for local LLMs is crucial for successful on-device AI. In summary, calculate the model size (params × bytes) plus the KV cache (which grows with context) to get total RAM needs. For example, a 7B FP16 model (~14 GB) will typically require 16–32 GB of RAM once you add cache and overhead. Smaller models (1–3B) need far less, while large models (33B+) quickly reach tens of gigabytes. By understanding these factors – model parameters, precision, sequence length, batch size – you can accurately plan your hardware.
Local LLMs enable private, offline AI on your own device (even low-end PCs). To simplify deployment, consider using tools like Kolosal.ai (which streamlines loading local models). These platforms handle much of the optimization under the hood. And remember, for even larger setups (multi-GPU, server-grade machines), check out our guides on running LLMs on mid- and high-end hardware.
Ready to try it yourself? Download Kolosal AI today and see how easy it is to run powerful LLMs locally with optimized memory usage.