Llama-3.1-70B-Instruct
Q4 GGUF download: MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF - 42.52 GB on disk, read 2026-09-27. VRAM figures above are estimates; this is the actual file size.
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
6.6
AA Index
Intelligence Index - Llama-3.1-70B-Instruct vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Benchmark data from Artificial Analysis · updated 2026-10-19.
Hosted API cost & speed
What it costs to rent this model instead of running it locally
| Metric | Value |
|---|---|
| Input (per 1M tokens) | $0.56 |
| Output (per 1M tokens) | $0.56 |
| Median speed (hosted API) | 33 tok/s |
Median across hosted providers, measured by Artificial Analysis - updated 2026-10-19. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.
Arena rating (LMArena)
Human preference wins from head-to-head chat battles
| Metric | Value |
|---|---|
| Overall arena rating | 1260.9 |
| Rank (overall) | #290 |
| Battle votes | 55,240 |
Overall rating published by LMArena on 2026-10-15 - a pairwise human-preference Elo across live chat battles; higher is better. Fetched 2026-10-19. Open-weights models compete head-to-head with proprietary ones here — no benchmark prompts, just what people pick.
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve meta-llama/Llama-3.1-70B-InstructNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Llama-3.1-70B-Instruct
Llama-3.1-70B-Instruct is a 70.6B-parameter llama3.1 model from meta-llama. At Q4_K_M it needs roughly 41 GB of VRAM, placing it in the 24-48gb hardware tier.
Specifications
| Spec | Value |
|---|---|
| Parameters | 70.6B |
| License | llama3.1 |
| Modalities | text |
| Released | 2024-07-16 |
| Weights | meta-llama/Llama-3.1-70B-Instruct |
VRAM requirements
| Quant | VRAM | Runs on |
|---|---|---|
| Q4_K_M | ~41 GB | RTX 6000 Ada, dual RTX 3090 |
| Q5_K_M | ~50 GB | A100 80GB, H100 |
| Q8_0 | ~76 GB | A100 80GB, H100 |
| FP16 | ~141 GB | multi-GPU / datacenter |
VRAM is estimated from parameter count; MoE models still need all weights resident.
How to run
vLLM:
vllm serve meta-llama/Llama-3.1-70B-InstructPopularity
Llama-3.1-70B-Instruct has 705,807 downloads in the last month on HuggingFace and 922 likes.
Frequently asked
Quick answers to common questions
How much VRAM does Llama-3.1-70B-Instruct need?
Llama-3.1-70B-Instruct with 70.6B parameters needs approximately 41 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Llama-3.1-70B-Instruct better than other meta-llama models?
Llama-3.1-70B-Instruct has 70.6B parameters with 8,192 context - a strong choice for general use.
What license is Llama-3.1-70B-Instruct under?
Llama-3.1-70B-Instruct is released under the llama3.1 license, making it suitable for most commercial and personal projects.
What hardware runs Llama-3.1-70B-Instruct well?
With 70.6B parameters, Llama-3.1-70B-Instruct requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for Llama-3.1-70B-Instruct?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~50 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
What models compete with Llama-3.1-70B-Instruct?
Llama-3.1-70B-Instruct competes with other 35B–106B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec