A good way to size GPU memory for a 70B model is to break it into four pieces:

1. Model weights
2. KV cache / context memory
3. Runtime overhead
4. Training/fine-tuning states if applicable

The exact number depends a lot on whether you’re doing inference, fine-tuning, or training, and on the precision/quantization.

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1) Model weights: the starting point

A 70B parameter model has about 70 billion parameters.

Approximate weight memory:

• FP16/BF16: 70B × 2 bytes ≈ 140 GB
• INT8: 70B × 1 byte ≈ 70 GB
• 4-bit quantized: 70B × 0.5 bytes ≈ 35 GB

In practice, add a little overhead for quantization scales, metadata, padding, etc., so real numbers are often a bit higher than the raw math.

Rule of thumb

• FP16/BF16 weights: plan for ~140–160 GB
• 8-bit weights: plan for ~75–90 GB
• 4-bit weights: plan for ~40–50 GB

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2) KV cache: grows with context length and batch size

For inference, the KV cache often becomes the next big memory consumer.

It scales with:

• number of layers
• hidden size / head count
• context length
• batch size
• whether you’re using multi-query/grouped-query attention

For a 70B-class model, KV cache can be:

• a few GB for short contexts and batch size 1
• tens of GB for long contexts or higher batch sizes

Practical rule of thumb

If you want to serve:

• 1 user, ~4k context: budget ~2–8 GB extra
• multiple concurrent users / 8k–32k context: budget ~10–40+ GB extra

This can dominate memory if you’re doing long-context serving.

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3) Runtime overhead

Frameworks need extra memory for:

• CUDA workspace
• activation buffers
• temporary tensors
• fragmentation / allocator overhead

A safe buffer is usually:

• 10–20% of total model+KV memory, or at least
• a few GB per GPU

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4) If training or fine-tuning, memory is much larger

Full training

Training a 70B model in FP16 is enormous because you also need:

• gradients
• optimizer states (e.g. Adam has momentum + variance)
• activation checkpointing buffers

A rough estimate:

• Weights: 140 GB
• Gradients: 140 GB
• Optimizer states: often 2–8× parameter size depending on optimizer/precision
• Activations: depends on batch/sequence length

So full training can easily require hundreds of GB to multiple TB of GPU memory, usually across many GPUs.

Fine-tuning

• LoRA / QLoRA: much smaller, often feasible on a few GPUs or even one large GPU
• Full fine-tuning: still very memory intensive

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Quick sizing examples

Inference examples

70B in FP16

• Weights: ~140 GB
• KV cache + overhead: ~10–30 GB
• Total: ~150–180+ GB

So you’d typically need:

• 2×80 GB GPUs minimum for comfortable inference
• or more if long context / high concurrency

70B in 4-bit

• Weights: ~35–50 GB
• KV cache + overhead: ~10–30 GB
• Total: ~45–80 GB

This may fit on:

• 1×80 GB GPU for modest context/concurrency
• 2×48 GB GPUs depending on serving stack and context length

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Simple sizing formula

For inference:

Total GPU memory ≈ weight size + KV cache + overhead

Where:

• weight size ≈ parameters × bytes per parameter
• KV cache ≈ context-dependent, often the variable that surprises people
• overhead ≈ 5–20 GB depending on runtime and fragmentation

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Practical recommendations

If you want a safe single-number estimate:

• FP16 70B inference: plan on 160–180 GB total
• 4-bit 70B inference: plan on 50–80 GB total

If you’re buying GPUs:

• 1×80 GB: feasible for 4-bit inference, not FP16
• 2×80 GB: good for FP16 inference and larger contexts
• 4×48 GB: also a common way to host large models

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Best way to be precise

Tell me:

1. Inference, fine-tuning, or training?
2. Precision: FP16/BF16, INT8, 4-bit?
3. Max context length
4. Batch size / concurrency
5. Model architecture if known

And I can estimate the GPU memory much more accurately.