Llama 3.1 8B 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 GiBfitsRTX 4060, RTX 3070, RX 7600
- 12 GiBfitsRTX 4070, RTX 3060 12GB, RTX 5070
- 16 GiBfitsRTX 4060 Ti 16GB, RTX 4080, RTX 5080
- 24 GiBfitsRTX 4090, RTX 3090, RX 7900 XTX
- 32 GiBfitsRTX 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 8B needs about 7.05 GiB of VRAM: 4.55 GiB of weights, 1.00 GiB of KV cache and 1.50 GiB of runtime overhead. The smallest common GPU tier that holds it is 8 GiB (RTX 4060, RTX 3070, RX 7600).
How much VRAM Llama 3.1 8B needs
Llama 3.1 8B, released by Meta in July 2024, is the most widely fine-tuned open model of its generation and the usual baseline for a local chat or coding assistant on a single consumer GPU.
With 8,192 tokens of context the estimate is 17.4 GiB at FP16, 10.4 GiB at Q8_0 and 7.05 GiB at Q4_K_M. FP16 needs a 24 GiB card, Q8_0 needs a 12 GiB card, and Q4_K_M needs an 8 GiB card. The weights are 8 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 8B has 32 layers, 8 KV heads and a head dimension of 128, read from its Hugging Face config.json, so at FP16 each token costs 128 KiB. That is 1.00 GiB at 8,192 tokens, and 16.0 GiB at the maximum of 131,072, where the Q4_K_M total becomes 22.1 GiB and needs a 24 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 7.05 GiB is the classic 8 GiB-card choice. With 12 GiB, Q8_0 at 10.4 GiB is close to lossless and worth the extra memory; FP16 at 17.4 GiB buys almost nothing over Q8_0 for inference and needs a 24 GiB card.
Pitfalls
Long context costs more than people expect: each token of cache is 128 KiB, so the full 131,072 tokens add 16.0 GiB, about three and a half times the Q4_K_M weights. Fine-tunes and merges keep this exact shape, so their numbers match this page.
DeepSeek-R1-Distill-Llama 8B shares this architecture byte for byte; Qwen2.5 7B is the closest alternative with a much smaller cache.
FAQ
Questions, answered.
Tap a question to expand the answer.
Can Llama 3.1 8B run on an 8 GB GPU?
Yes at Q4_K_M: 7.05 GiB at 8,192 tokens of context. At 16,384 tokens it rises to 8.05 GiB and no longer fits, and Q8_0 at 10.4 GiB needs a 12 GiB card.
How much VRAM does Llama 3.1 8B need at 128k context?
22.1 GiB at Q4_K_M, because the KV cache grows to 16.0 GiB at 131,072 tokens. That needs a 24 GiB card, unless the runtime quantizes the cache.
Is FP16 worth it for Llama 3.1 8B?
Rarely for inference. FP16 needs 17.4 GiB against 10.4 GiB for Q8_0, and Q8_0's output is practically indistinguishable. FP16 matters for fine-tuning, which needs far more memory than this estimate anyway.
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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.