LLaMA 3 70B Context Length: VRAM Impact by GPU

LLaMA 3 70B and Context Length

LLaMA 3 70B is among the most powerful open-weight LLMs, but its size means that even short contexts already require dedicated GPU hosting with multiple cards. Extending the context window from the default 8K to 32K or 128K tokens adds substantial KV cache overhead on top of the ~140 GB model weights at FP16. This guide provides exact VRAM figures so you can plan your multi-GPU cluster accurately.

For baseline model weight requirements see our LLaMA 3 VRAM requirements overview. This page focuses specifically on how context length scales VRAM beyond those base figures.

VRAM Usage from 512 to 128K Tokens

The table below shows total VRAM for a single-request batch running LLaMA 3 70B at FP16 and INT4 (GPTQ 4-bit). KV cache grows with 64 KV heads across 80 layers, consuming roughly 1.25 GB per 1,000 tokens at FP16.

At the default 8K context, FP16 demands roughly 151 GB — already requiring at least 2x RTX 6000 Pro 96 GB or 4x RTX 5090. Pushing to 32K nearly doubles the overhead. For a visual breakdown of this scaling pattern, see our context length VRAM guide.

Multi-GPU Requirements

Since LLaMA 3 70B exceeds any single consumer GPU at FP16, tensor parallelism across multiple GPUs is required. The table below maps context lengths to practical GPU configurations.

For details on splitting models across GPUs, see our model sharding guide for 70B+ models.

Quantisation to Extend Context

INT4 quantisation (GPTQ or AWQ) reduces the 70B model from ~140 GB to ~37 GB, freeing over 100 GB that can be used for KV cache. This makes extended contexts far more practical on consumer-grade multi-GPU setups.

• GPTQ 4-bit: best throughput on GPU via ExLlama kernels, widely supported in vLLM and text-generation-inference.
• AWQ 4-bit: slightly better quality preservation at INT4, excellent vLLM integration.
• GGUF Q4_K_M: useful for CPU-offload hybrid setups but slower on pure GPU deployments.

Even with quantised weights, the KV cache remains in FP16 by default. Enabling FP8 KV cache in vLLM halves the cache footprint, making 64K context feasible on a 4x RTX 3090 setup with INT4 weights.

Memory Optimisation Tips

Running 70B models at long contexts requires every available optimisation:

• FlashAttention 2: mandatory for sequences above 8K — eliminates O(n squared) attention memory overhead.
• Quantised KV cache (FP8): cuts cache memory in half with minimal quality loss.
• Continuous batching: ensures GPU utilisation stays high even when serving multiple users at varied context lengths.
• PagedAttention: built into vLLM, eliminates memory fragmentation from variable-length KV caches. See our vLLM optimisation guide for configuration details.

Conclusion

LLaMA 3 70B is a multi-GPU model at any context length, and extending beyond the default 8K dramatically increases VRAM requirements. Plan for 151 GB at 8K (FP16) and over 300 GB at 128K. Quantisation is the most effective lever — INT4 with FP8 KV cache can reduce total memory by over 60%, bringing long-context deployments within reach of consumer GPU clusters. For speed comparisons, consult our best GPU for LLM inference benchmarks.