Llama 3.1 70B is the workhorse of self-hosted AI in 2024-2026 — competitive with GPT-4-class proprietary models on most benchmarks. At Q4_K_M it fits in a single RTX 4090 or 5090 (24-32 GB) with reduced context, or comfortably on dual 24 GB cards. The trade-off versus 8B is roughly 5-8x slower tok/s on identical hardware.
Llama 3.1 70B needs at minimum 42.4 GB of VRAM at Q4_K_M quantization (154 GB at FP16). The cheapest GPU that comfortably fits with KV-cache headroom is the Apple Mac mini (M4 Pro, 64 GB) (64 GB VRAM, $2,199 MSRP). Measured throughput hits 540 tok/s on NVIDIA H200 141GB. This model fits a single consumer GPU under 48 GB, so a one-card build works.
Weights only. Add ~20-30% for KV cache at typical context lengths.
| Quant | VRAM | Quality | Notes |
|---|---|---|---|
| FP16 | 154.0 GB | Reference | Training-precision weights |
| Q8_0 | 77.0 GB | Near-lossless | 8-bit, ~0.1% perplexity hit |
| Q6_K | 62.4 GB | Very high | 6-bit, near Q8 quality |
| Q5_K_M | 53.1 GB | High | Strong middle ground |
| Q4_K_M | 42.4 GB | Balanced (recommended) | Default for local deployments |
| Q4_0 | 43.3 GB | Legacy 4-bit | Older GGUF, kept for compatibility |
| Q3_K_M | 33.1 GB | Lossy | When VRAM is very tight |
| Q2_K | 24.6 GB | Extreme | Rescue option, quality degrades visibly |
Filtered from MyAIHardware's GPU database. FP16 needs 154.0 GB; Q4_K_M needs 42.4 GB.
52 measurement(s) for this model from MyAIHardware's benchmark database.
| Device | Speed | Quant | Context | Power |
|---|---|---|---|---|
| NVIDIA H200 141GB | 540 tok/s | Q4_K_M | 4096 | 700W |
| Cerebras WSE-3 (CS-3) | 450 tok/s | FP16 | 8192 | 23000W |
| 8× NVIDIA H100 SXM5 80GB | 412 tok/s | Q4_K_M | 8192 | 5600W |
| NVIDIA H100 SXM5 80GB | 410 tok/s | Q4_K_M | 4096 | 700W |
| AMD Instinct MI300X 192GB | 380 tok/s | Q4_K_M | 4096 | 750W |
| Groq LPU (8-chip rack) | 285 tok/s | FP16 | 8192 | 1720W |
| NVIDIA B200 192GB | 138 tok/s | Q4_K_M | 8192 | 1000W |
| Google TPU v5p | 110 tok/s | INT8 | 8192 | 700W |
| NVIDIA B200 192GB | 98 tok/s | Q8_0 | 8192 | 1000W |
| NVIDIA GH200 Grace Hopper 480GB | 95 tok/s | Q4_K_M | 8192 | 1000W |
| AWS Trainium2 | 92 tok/s | INT8 | 8192 | 500W |
| NVIDIA H200 141GB | 84 tok/s | Q4_K_M | 8192 | 700W |
| NVIDIA GeForce RTX 5090 32GB | 78 tok/s | Q4_K_M | 4096 | 575W |
| AMD Instinct MI300X 192GB | 72 tok/s | Q4_K_M | 8192 | 750W |
| NVIDIA H100 SXM5 80GB | 66 tok/s | Q4_K_M | 4096 | 700W |
| Google TPU v5e | 62 tok/s | INT8 | 4096 | 170W |
| NVIDIA H200 141GB | 58 tok/s | Q8_0 | 8192 | 700W |
| AWS Trainium2 | 58 tok/s | Q4_K_M | 8192 | 500W |
| AMD Instinct MI250X 128GB | 52 tok/s | Q4_K_M | 8192 | 560W |
| AMD Instinct MI300X 192GB | 48 tok/s | Q8_0 | 8192 | 750W |
Official weights + popular runtime tags.
ollama pull llama3.1:70blms get bartowski/Meta-Llama-3.1-70B-Instruct-GGUFStep-by-step guides that use this model.
Models in a similar size or capability class.
Use these in papers, blog posts, or internal docs.
@misc{llama-3-1-70b-2024,
title={Llama 3.1 70B},
author={Meta AI},
year={2024},
url={https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct}
}Meta AI (2024). Llama 3.1 70B [Model card]. Hugging Face. https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct
Llama 3.1 70B (Llama 3.1, Meta AI, 2024). Available at https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct.