LLM GPU VRAM Calculator: Model Parameters, Quantization & Context
Running large language models locally means fitting both the model weights and the growing KV cache attention memory into your GPU's VRAM. This calculator estimates total VRAM demand across common parameter sizes, quantization formats and context lengths, then recommends a GPU tier so you don't buy more (or less) hardware than you need.
A 8B model at Q4_K_M with 8,192-token context needs roughly 70,481.6GB of VRAM — a Multi-GPU (>80GB) should run it comfortably.
| Component | Value |
|---|---|
| Parameters | 8B (8B) |
| Quantization | Q4_K_M (4.5 bits/param) |
| Context length | 8,192 tokens |
| Batch size | 1 |
| Estimated layers | 21 |
| Weight VRAM | 4.19 GB |
| KV cache VRAM | 70,476.98 GB |
| Runtime overhead (~10%) | 0.42 GB |
| Total VRAM | 70,481.59 GB |
Formula & step-by-step maths
- Bits_per_param
- Quantization precision in bits per parameter
- kv_heads
- Number of key-value attention heads (architecture-dependent)
- head_dim
- Dimension per attention head, typically 128
Why quantization matters so much
A 7B parameter model at full FP16 precision needs about 14GB just for weights, but the same model quantized to Q4_K_M needs roughly 4GB — a nearly 4x reduction with modest quality loss. This is why almost every locally-run open model in the community is distributed as a quantized GGUF file rather than raw FP16 weights.
The often-overlooked KV cache cost
Every token generated adds a small slice to the key-value cache that attention layers reuse across the sequence, and that cache grows linearly with context length and batch size. At 128k context, KV cache VRAM can rival or exceed the model weights themselves, which is the main reason long-context inference needs far more VRAM than the base model size suggests.
Reading the GPU tier recommendation
Consumer GPUs cluster around 8GB, 12GB, 16GB and 24GB of VRAM, while prosumer and datacenter cards jump to 48GB and 80GB. This tool rounds your estimated requirement up to the next standard tier, leaving some headroom for the OS, other running applications, and inference-engine overhead beyond raw weight and cache math.
Estimating architecture without exact specs
Publicly known layer counts, hidden dimensions and head counts vary between model families even at the same parameter count, so this calculator uses reasonable scaling approximations based on typical transformer architectures. For a model where you know the exact config (from its Hugging Face config.json), treat this tool's output as a close estimate rather than an exact figure.
Approximate VRAM by model size and quantization (8k context)
| Params | FP16 | Q8_0 | Q5_K_M | Q4_K_M | Q2_K |
|---|---|---|---|---|---|
| 7B | ~14 GB | ~7.4 GB | ~4.8 GB | ~3.9 GB | ~2.3 GB |
| 8B | ~16 GB | ~8.5 GB | ~5.5 GB | ~4.5 GB | ~2.6 GB |
| 13B | ~26 GB | ~13.8 GB | ~8.9 GB | ~7.3 GB | ~4.2 GB |
| 34B | ~68 GB | ~36.1 GB | ~23.4 GB | ~19.1 GB | ~11.0 GB |
| 70B | ~140 GB | ~74.4 GB | ~48.1 GB | ~39.4 GB | ~22.8 GB |
People also ask
How much VRAM do I need to run a 70B model?
At Q4_K_M quantization, a 70B model needs roughly 40GB of VRAM for weights alone, plus KV cache and overhead, meaning it typically requires a single 48GB card or two 24GB GPUs in combination.
What quantization level should I use for local inference?
Q4_K_M is the community standard sweet spot, offering strong quality retention with roughly a quarter of the VRAM footprint of FP16; Q5_K_M is a good step up if you have spare VRAM headroom.
Why does context length affect VRAM so much?
The KV cache that stores attention keys and values for every previous token grows linearly with context length, so doubling your context window roughly doubles that portion of VRAM usage.
Can I run a 13B model on a 12GB GPU?
At Q4_K_M quantization yes, since weights need roughly 7.3GB, leaving enough headroom for a moderate context window and runtime overhead on a 12GB card.
Does batch size matter for single-user chat use?
For a single interactive chat session batch size is effectively 1; it mainly matters for serving multiple concurrent users or running parallel generation requests.
Is FP16 ever worth the extra VRAM cost?
FP16 preserves the most model quality and is preferred for fine-tuning or benchmarking research, but for everyday local inference, quantized formats like Q4_K_M or Q5_K_M offer a much better VRAM-to-quality tradeoff.
Why do two different 7B models need different amounts of VRAM?
Architecture details like hidden dimension, layer count, and vocabulary size vary between model families even at the same parameter count, causing modest differences in real-world VRAM needs beyond this estimate.
What GPU should I buy for running 8B models comfortably?
A 12GB or 16GB consumer GPU comfortably runs 8B models at Q4 to Q5 quantization with moderate context length, leaving room to experiment with longer contexts or slightly larger models later.
Does CPU offloading reduce the VRAM requirement?
Yes, tools like llama.cpp can offload some model layers to system RAM and CPU, trading inference speed for lower VRAM usage, which is useful when your GPU falls just short of the required amount.
How accurate is this VRAM estimate compared to real-world usage?
It's a solid planning estimate within roughly 10-20%, but actual usage varies by inference engine, CUDA/kernel overhead, and whether flash-attention or paged KV cache optimizations are enabled.
Three worked examples
Same engine, three different starting points — useful if you want to see how sensitive the answer is before you type your own numbers in.
Example 1: batch size 1 sequences, model parameters "7B"
On the lower / more conservative end. A 7B model at Q4_K_M with 8,192-token context needs roughly 61,671.5GB of VRAM — a Multi-GPU (>80GB) should run it comfortably.
Example 2: batch size 1 sequences, model parameters "8B"
A typical middle-of-the-road setup. A 8B model at Q4_K_M with 8,192-token context needs roughly 70,481.6GB of VRAM — a Multi-GPU (>80GB) should run it comfortably.
Example 3: batch size 1.3 sequences, model parameters "13B"
On the higher / more demanding end. A 13B model at Q4_K_M with 8,192-token context needs roughly 148,890.7GB of VRAM — a Multi-GPU (>80GB) should run it comfortably.
Quick answers about the LLM GPU VRAM Calculator
What exactly does the LLM GPU VRAM Calculator work out?
Running large language models locally means fitting both the model weights and the growing KV cache attention memory into your GPU's VRAM. You enter model parameters, quantization, context length and batch size and the result panel updates straight away, so you can compare two or three versions of the same question in a few seconds.
What do I need before I start?
Only 4 fields: model parameters, quantization, context length and batch size. Nothing else is needed and nothing is stored.
How is it calculated — why quantization matters so much?
A 7B parameter model at full FP16 precision needs about 14GB just for weights, but the same model quantized to Q4_K_M needs roughly 4GB — a nearly 4x reduction with modest quality loss. The same maths runs inside this page, so hand-checking the result on paper gives you the identical figure.
Why do two calculators give me different answers for lLM GPU VRAM?
Every token generated adds a small slice to the key-value cache that attention layers reuse across the sequence, and that cache grows linearly with context length and batch size. Different sites pick different assumptions, so always check which method a calculator states before you trust the gap between two numbers.
What does the "Approximate VRAM by model size and quantization (8k context)" table on this page tell me?
It is the reference range this tool works against — 5 rows from "7B" (~14 GB) up to "70B" (~140 GB). Use it to sanity-check whether the number you just calculated sits where you expected it to.
Which model parameters should I pick?
The dropdown offers 5 choices — 7B, 8B, 13B, 34B and 70B. Pick the one that matches your real situation rather than the one you would like to be true; model parameters usually moves the final figure more than any other single input, so it is worth running it twice with the option above and below your guess.
Do I have to press a button or reload the page to see the result?
No. LLM GPU VRAM Calculator runs completely inside your browser, so the moment you change a value the cards recalculate — there is no submit step, no page reload and no waiting for a server round trip. That also means it keeps working on a weak or intermittent mobile connection.
Is it free, and do you keep what I type?
It is free with no sign-up, no app install and no usage limit. Nothing you enter into LLM GPU VRAM Calculator leaves your device — the calculation is JavaScript running locally, so there is no upload of your figures to DrHint or anyone else.
Can I use it on a phone?
Yes — the layout stacks to a single column on small screens and the number fields open the numeric keypad on both Android and iOS. Many people bookmark this page or add it to their home screen and re-open it whenever the question comes up.
Anything to be careful about with the result?
At Q4_K_M quantization, a 70B model needs roughly 40GB of VRAM for weights alone, plus KV cache and overhead, meaning it typically requires a single 48GB card or two 24GB GPUs in combination. Treat the output as a well-grounded estimate for planning, not as a professional, legal or medical decision on its own.
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