Llama 3.1 405B
405 B parameters · 128K context · Llama 3.1 Community License · released 2024-07-23 by Meta.
Llama 3.1 405B needs about 810 GB at FP16: no single GPU can hold it. Serving requires an 8-GPU HGX baseboard (INT8/INT4) or a full rack (NVL72, TPU pod) at FP16.
Where this model sits
Frontier-class open-weight. Trades size for quality: needs multi-GPU serving even at INT4, but crosses GPT-4 on many reasoning + coding benchmarks.
What runs it (production serving)
Datacenter GPUs and rack designs sized for Llama 3.1 405B. Consumer cards are not practical at this size.
Smallest datacenter GPU that fits (INT4)
Multi-GPU / rack-scale (FP16)
Memory at different context lengths
Weights are fixed. The KV cache grows linearly with context length and can rival the weights at full window. Most guides only quote weights, then hand-wave a 15% overhead, which is only true at short context.
| Context | FP16 total | INT8 total | INT4 total |
|---|---|---|---|
| 4K (short chat) | 810 + 2.0 = 812.0 GB | 405 + 1.0 = 406.0 GB | 203 + 1.0 = 203.5 GB |
| 32K (typical RAG) | 810 + 15.8 = 825.8 GB | 405 + 7.9 = 412.9 GB | 203 + 7.9 = 210.4 GB |
| 128K (full window) | 810 + 63.0 = 873.0 GB | 405 + 31.5 = 436.5 GB | 203 + 31.5 = 234.0 GB |
KV bytes per token = 2 × 126 layers × 8 KV heads × 128 head-dim × precision bytes. INT4 rows keep the KV cache at INT8 (typical serving default).
Quality benchmarks
Vendor-reported scores from the release blog or tech report. Every row cites its source.
| Benchmark | Score | Source | Date |
|---|---|---|---|
| MMLU (5-shot) | 88.6 | Meta Llama 3.1 blog | 2024-07-23 |
| HumanEval | 89.0 | Meta Llama 3.1 blog | 2024-07-23 |
| MATH (0-shot) | 73.8 | Meta Llama 3.1 blog | 2024-07-23 |
| GPQA | 51.1 | Meta Llama 3.1 blog | 2024-07-23 |
Speed and cost to serve
What cloud providers currently charge to serve Llama 3.1 405B. Tokens per second is what the provider publishes on their own page. Batch size, prompt length, and inference engine can shift these numbers a lot; treat as directional.
| Provider | Hardware | Speed | $/M in | $/M out | As of |
|---|---|---|---|---|---|
| Together AI Instruct Turbo | 8x H100 | — | $3.50 | $3.50 | 2025-08-01 |
| AWS Bedrock Instruct | — | — | $5.32 | $16.00 | 2025-08-01 |
| Fireworks AI | H100 nodes | — | $3.00 | $3.00 | 2025-08-01 |
Cheapest option in this list for output-heavy workloads: Fireworks AI at $3.00/M out. Fastest published: not enough data.
License and variants
- License
- Llama 3.1 Community License
- The gotcha
- 700M-MAU commercial threshold, same as 8B.
- Variants
- Base · Instruct
- Context window
- 131,072 tokens (128K)
- Architecture
- dense
Compare with
Model card: https://huggingface.co/meta-llama/Llama-3.1-405B · Release: Meta announcement
See also: every open-source LLM we track · all AI chips · rack designs.