Why can't my 16 GB consumer GPU serve LLaMA-7B at batch size 8?

7B × 2 bytes (FP16 weights) = 14 GB. Add ~1 GB for the KV cache at 2048 context with batch 1, ~500 MB for activations, and ~2 GB framework overhead. You're already at ~17.5 GB — over the 16 GB limit. At batch 8, the KV cache scales linearly to ~8 GB, pushing total VRAM past 24 GB. You need INT4 quantization or a 24 GB GPU like an RTX 4090.

How much VRAM does a 70B parameter model need for inference?

At FP16: 140 GB just for weights, plus 10-40 GB for KV cache depending on context length and batch size. Total: 150-180 GB. That requires 2× A100 80GB or 2× H100 80GB with tensor parallelism. At INT4 quantization, weights drop to ~35 GB, making the model fit on a single A100 80GB for batch-1 inference at moderate context lengths.

What quantization level should I use for LLM serving?

FP16 is the reference quality baseline. INT8 (8-bit) reduces VRAM by ~50% with negligible quality loss for most tasks. INT4 (4-bit) reduces VRAM by ~75% but may show quality degradation on reasoning and math tasks. GPTQ and AWQ are the most common INT4 methods. For chatbots and RAG, INT4 is usually fine. For code generation or math, stick with INT8 or FP16.

LLM Parameters to FP16 VRAM Calculator

🧠 Model Configuration

Enter your dense LLM specifications. The engine computes raw weight memory, applies framework overhead, and recommends GPU topology.

Model Parameter Size (Billion) e.g. Llama-2-7B → 7

Precision Bits FP16=16, FP32=32

Overhead % Framework + KV Cache 20–30%

FP16 VRAM Estimation: Why Precision Matters for GPU Provisioning

Deploying large language models for inference requires careful GPU memory planning. At FP16 (half-precision), each parameter consumes 2 bytes of memory. A 7-billion-parameter model like Llama-2-7B needs approximately 14 GB just for the raw weights. But the total VRAM footprint is always larger than the raw weight storage due to framework runtime overhead, CUDA context, and the all-important KV cache.

How This Calculator Works

This tool computes VRAM requirements using the standard dense-model memory formula and then applies a configurable overhead multiplier to account for real-world inference overhead:

Component	Formula	Description
Raw Weights	`Parameters × Precision Bits ÷ 8`	Converts parameter count and bit-width into raw byte storage. A 7B parameter model at FP16 (16 bits = 2 bytes) requires 14 GiB of raw weight storage.
Overhead Multiplier	`1 + Overhead%`	Accounts for KV cache (key-value attention states), CUDA context allocations, framework runtime (PyTorch/vLLM/TGI), and activation memory. Production deployments typically observe 20–30% overhead beyond raw weights.
Total VRAM	`Raw Weights × (1 + Overhead%)`	The final per-GPU or aggregate VRAM requirement. This number determines how many GPUs you need and whether tensor parallelism or pipeline parallelism is necessary.

The 20–30% Overhead Rule Explained

The overhead buffer is not arbitrary — it reflects three real-world memory consumers that sit alongside model weights in GPU memory:

KV Cache (Key-Value Cache): The attention mechanism stores key and value tensors for every token in the context window across every transformer layer. For long-context inference (32K+ tokens), the KV cache can rival or exceed the weight memory. Use our companion KV Cache Inflation Calculator to model this separately.

CUDA Context and Driver Overhead: NVIDIA drivers, CUDA runtime, and cuBLAS/cuDNN libraries consume a fixed baseline of GPU memory (typically 300–700 MB per GPU). This overhead is independent of model size and must be accounted for in every deployment.

Framework Runtime: Inference engines like vLLM, Text Generation Inference (TGI), and TensorRT-LLM allocate additional memory for batching queues, communication buffers (NCCL), and scheduling metadata. PyTorch itself reserves a CUDA memory pool that may appear as "allocated but unused" in nvidia-smi.

GPU Selection Guide

Once you know your total VRAM requirement, GPU selection becomes a capacity-matching exercise:

GPU Model	VRAM	Best Fit For	Limitations
NVIDIA A100 80GB	80 GB	70B-class models at FP16 (requires 2–4 GPUs with tensor parallelism). Industry standard for high-throughput production serving.	Premium pricing. Overkill for sub-13B models.
NVIDIA A6000 (Ampere)	48 GB	13B-class models at FP16 on a single GPU. 70B models with INT4 quantization.	Lower memory bandwidth than A100 (768 GB/s vs. 2,039 GB/s).
NVIDIA RTX 4090	24 GB	7B-class models at FP16 on a single GPU. Excellent cost-per-GB for hobbyist and small-scale deployments.	Consumer card — no NVLink, limited to single-GPU inference without model parallelism. 24 GB is insufficient for 13B+ FP16 models.

Why FP16 Instead of FP32?

FP16 (half-precision floating point) has become the de facto standard for LLM inference because it halves memory consumption relative to FP32 with negligible accuracy degradation for most tasks. Modern GPU architectures (Ampere, Hopper, Ada Lovelace) include dedicated FP16 tensor cores that deliver 2–4× higher throughput than FP32. Unless you are doing scientific computing that requires full 32-bit precision, FP16 is the correct default for LLM serving.

📎 Embed This Calculator

Embed the LLM VRAM estimator on your ML team wiki, model card, or GPU provisioning doc. No API key, no tracking — everything runs in the visitor's browser.

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