Llama 3.1 70B VRAM: what it needs at Q4_K_M, Q8_0 and FP16
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KV cache is counted at FP16. Overhead is a fixed 1.5 GiB estimate for the runtime and compute buffers. A custom size borrows the architecture of the nearest preset.
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GPU tiers
- 8 GiBtoo smallRTX 4060, RTX 3070, RX 7600
- 12 GiBtoo smallRTX 4070, RTX 3060 12GB, RTX 5070
- 16 GiBtoo smallRTX 4060 Ti 16GB, RTX 4080, RTX 5080
- 24 GiBtoo smallRTX 4090, RTX 3090, RX 7900 XTX
- 32 GiBtoo smallRTX 5090, V100 32GB
- 48 GiBfitsRTX A6000, RTX 6000 Ada, L40S
- 80 GiBfitsA100 80GB, H100 80GB
At Q4_K_M with an 8,192-token context, Llama 3.1 70B needs about 43.8 GiB of VRAM: 39.8 GiB of weights, 2.50 GiB of KV cache and 1.50 GiB of runtime overhead. The smallest common GPU tier that holds it is 48 GiB (RTX A6000, RTX 6000 Ada, L40S).
How much VRAM Llama 3.1 70B needs
Llama 3.1 70B is Meta's mid-size flagship from July 2024, the model people move to when an 8B answer is not good enough and a hosted API is not an option.
With 8,192 tokens of context the estimate is 134 GiB at FP16, 73.3 GiB at Q8_0 and 43.8 GiB at Q4_K_M. FP16 needs more than a single 80 GiB card, Q8_0 needs an 80 GiB card, and Q4_K_M needs a 48 GiB card. The weights are 70 billion parameters times the bits per weight of each format (16, 8.5 and 4.89), divided by eight; everything is in GiB, the unit GPU memory is sold in.
Context length and the KV cache
The KV cache stores a key and a value vector for every token in every layer. Llama 3.1 70B has 80 layers, 8 KV heads and a head dimension of 128, read from its Hugging Face config.json, so at FP16 each token costs 320 KiB. That is 2.50 GiB at 8,192 tokens, and 40.0 GiB at the maximum of 131,072, where the Q4_K_M total becomes 81.3 GiB and needs more than a single 80 GiB card. The cache does not shrink with weight quantization; only a runtime option that quantizes the cache itself does.
Which quantization to pick
Q4_K_M at 43.8 GiB is the realistic target and fits a 48 GiB card or two 24 GiB cards split across layers. Q8_0 at 73.3 GiB needs an 80 GiB card. Q3_K_M at 36.6 GiB is the floor before quality falls off noticeably.
Pitfalls
FP16 at 134 GiB does not fit any single card in the tier list, and splitting across GPUs adds a little overhead per device that this estimate does not model. On Apple Silicon, remember the GPU gets about three quarters of unified memory by default, so a 64 GB Mac has roughly 48 GB usable, which is tight for Q4_K_M.
DeepSeek-R1-Distill-Llama 70B uses the same shape; Qwen2.5 72B is the nearest rival at a similar size.
FAQ
Questions, answered.
Tap a question to expand the answer.
Can Llama 3.1 70B run on two RTX 3090s?
At Q4_K_M, yes: the estimate is 43.8 GiB at 8,192 tokens, under the 48 GiB the pair provides. At 32,768 tokens it climbs to 51.3 GiB and no longer fits.
How much VRAM does Llama 3.1 70B need at FP16?
134 GiB at 8,192 tokens, of which 130 GiB is weights. That is more than a single 80 GiB A100 or H100, so FP16 means at least two of them.
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Estimates only; the KV cache is sized at FP16 and the overhead is a fixed allowance, so always confirm against your own runtime’s report before buying hardware. OpsCanopy is free and open.