Tools / LLM GPU calculator / Llama 3.1 8B
Llama 3.1 8B VRAM requirements
GPU memory for the weights and KV cache at FP16, FP8 and 4-bit, and how many GPUs that takes. Figures from the model's config.json.
Short answer
Llama 3.1 8B needs about 19 GB of GPU memory at FP16/BF16, 10 GB at FP8 and 6.1 GB at 4-bit to serve one user at an 8K-token context. Its 8.03B parameters take 16.1 GB at 16-bit, and that conversation adds 1.1 GB of KV cache. On NVIDIA H100 80 GB GPUs that is 1 GPU at FP16 or FP8; for a desktop, at 4-bit it fits a single 24 GB GeForce RTX 4090.
Updated . KV cache at FP16, plus 10% headroom for activations and the runtime.
GPU memory by precision
| Weights | Weight memory | 1 user, 8K context | 32 users, 8K context | 1 user, 128K context |
|---|---|---|---|---|
| FP16/BF16 | 16.1 GB | 19 GB | 56 GB | 37 GB |
| FP8 | 8 GB | 10 GB | 47 GB | 28 GB |
| 4-bit (INT4) | 4.4 GB | 6.1 GB | 43 GB | 24 GB |
Required memory = (weights + KV cache) ÷ 0.9. An FP8 KV cache halves the cache share; set it in the calculator.
How many GPUs for one user at 8K context
| GPU | Memory | FP16/BF16 | FP8 | 4-bit (INT4) |
|---|---|---|---|---|
| Data center · tensor parallel | ||||
| NVIDIA L40S | 48 GB | 1 GPU | 1 GPU | 1 GPU |
| NVIDIA RTX PRO 6000 Blackwell Server Edition | 96 GB | 1 GPU | 1 GPU | 1 GPU |
| NVIDIA H100 SXM | 80 GB | 1 GPU | 1 GPU | 1 GPU |
| NVIDIA H100 NVL | 94 GB | 1 GPU | 1 GPU | 1 GPU |
| NVIDIA H200 | 141 GB | 1 GPU | 1 GPU | 1 GPU |
| NVIDIA B200 | 180 GB | 1 GPU | 1 GPU | 1 GPU |
| NVIDIA B300 | 288 GB | 1 GPU | 1 GPU | 1 GPU |
| AMD Instinct MI300X | 192 GB | 1 GPU | 1 GPU | 1 GPU |
| AMD Instinct MI355X | 288 GB | 1 GPU | 1 GPU | 1 GPU |
| Workstation & consumer · layer split | ||||
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 96 GB | 1 card | 1 card | 1 card |
| NVIDIA GeForce RTX 3090 | 24 GB | 1 card | 1 card | 1 card |
| NVIDIA GeForce RTX 4090 | 24 GB | 1 card | 1 card | 1 card |
| NVIDIA GeForce RTX 5090 | 32 GB | 1 card | 1 card | 1 card |
| Apple Mac Studio M3 Ultra / M5 Ultra | 96 GB (72 usable) | 1 Mac | 1 Mac | 1 Mac |
| Apple Mac Studio M3 Ultra / M5 Ultra | 256 GB (192 usable) | 1 Mac | 1 Mac | 1 Mac |
| Apple Mac Studio M5 Ultra | 512 GB (384 usable) | 1 Mac | 1 Mac | 1 Mac |
Data center GPUs are counted at a tensor-parallel size (TP) that divides the model’s attention heads, within one 8-GPU server. Workstation and consumer cards are counted for a layer split, which works with any number of cards. Mac Studio counts about 75% of unified memory as usable by the GPU. Capacity only: speed depends on memory bandwidth and the serving stack.
Memory is step one. Throughput, latency, power and the network decide the real build.
Size my actual workloadLlama 3.1 8B architecture
- Parameters
- 8.03B
- Layers with a KV cache
- 32
- Attention heads / KV heads
- 32 / 8
- Head dimension
- 128
- Attention
- Grouped-query attention (4 query heads per KV head)
- Maximum context
- 131,072 tokens
- KV cache per token (FP16)
- 2 × 32 layers × 8 KV heads × 128 head dim × 2 bytes = 131,072 bytes (0.13 MB) per token
Source: meta-llama/Llama-3.1-8B-Instruct config.json on Hugging Face. Parameter counts are the Hub’s count of the repo’s safetensors files, or the model card’s figure.
Questions about Llama 3.1 8B
How much VRAM does Llama 3.1 8B need?
About 19 GB at FP16/BF16, 10 GB at FP8 and 6.1 GB at 4-bit, for one user at an 8K-token context. That is 16.1 GB, 8 GB or 4.4 GB of weights, plus 1.1 GB of KV cache, plus 10% headroom for activations and the runtime. Serving 32 users at 8K each takes 56 GB at FP16 or 47 GB at FP8, because every conversation keeps its own KV cache.
How many H100 GPUs does Llama 3.1 8B need?
At FP16/BF16 it takes 1 GPU, for one user at 8K or for 32 users. At FP8, 1 GPU, for one user at 8K or for 32 users. One user at FP8 takes 1 GPU on H200 (141 GB) and 1 GPU on B200 (180 GB).
Can Llama 3.1 8B run on an RTX 4090 or RTX 5090?
At 4-bit it needs about 6.1 GB for one user at 8K: a single RTX 4090 (24 GB) or a single RTX 5090 (32 GB). At FP8 (10 GB) it takes a single RTX 4090 (24 GB) or a single RTX 5090 (32 GB).
Can Llama 3.1 8B run on a Mac Studio?
macOS lets the GPU use about 75% of unified memory by default: about 72 GB of a 96 GB, 192 GB of a 256 GB and 384 GB of a 512 GB Mac Studio. At 4-bit Llama 3.1 8B needs 6.1 GB for one user at 8K, so it fits one 96 GB Mac Studio; at FP8 (10 GB) it fits one 96 GB Mac Studio.
How large is Llama 3.1 8B's KV cache?
At FP16, 2 × 32 layers × 8 KV heads × 128 head dim × 2 bytes = 131,072 bytes (0.13 MB) per token. It uses grouped-query attention: 4 query heads share each KV head, which makes the cache 4× smaller than full multi-head attention. An 8K-token conversation holds 1.1 GB at FP16, and a 128K-token one holds 17 GB.
What is Llama 3.1 8B's maximum context length?
131,072 tokens, according to its Hugging Face configuration. One user at that full length holds 17 GB of KV cache at FP16, for 28 GB in total with FP8 weights, or 18 GB with an FP8 KV cache as well.
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Not listed? The LLM GPU calculator sizes any Hugging Face model from its ID, link or config.json.
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