Llama 3.1 70B
By Meta · United States
Updated 2026-07-13
Overview
Meta's Llama 3.1 70B, the open-weight model that first felt like a credible GPT-4 alternative. Needs serious hardware — think dual 3090s or an A100.
When to pick this model
- On-prem deployments needing frontier-adjacent quality
- Long-context reasoning and document workloads up to 128k tokens
- Self-hosted alternatives to GPT-4 class APIs
- Multi-GPU inference servers already provisioned for 70B-class models
- Fine-tuning when you need a strong base for domain adaptation
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 40 GB |
| Q5_K_M | 48 GB |
| Q8_0 | 75 GB |
| FP16 (no quantization) | 140 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Llama 3.1 70B spills past single consumer GPUs even at Q4_K_M (40 GB) — think dual-GPU or workstation cards. Stepping up to Q8_0 nearly doubles the footprint to 75 GB, and unquantized FP16 weights take 140 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Llama 3.1 70B needs roughly 64 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 1 tokens/sec on entry-level GPUs, on the order of 6 tokens/sec on a mid-range card, and up to 20 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Llama 3.1 70B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Llama 3.1 70B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 40 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 40 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 40 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 40 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 40 GB at Q4_K_M |
Which GPU should you buy to run Llama 3.1 70B?
To run Llama 3.1 70B locally at Q4, you need ~40 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| MMLU | 86 |
| GPQA | 48 |
| HumanEval | 80.5 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put Llama 3.1 70B in context: its MMLU score of 86 ranks #4 of the 34 catalog models with a published MMLU result (catalog median 73.4); its HumanEval score of 80.5 ranks #15 of the 26 catalog models with a published HumanEval result (catalog median 81.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- Benchmark-leading quality for open-weight 70B
- 128k context
- Strong reasoning and code generation
- Mature serving stack in vLLM, TGI, llama.cpp
Limitations
- ~40GB VRAM at Q4 — minimum two 24GB GPUs
- Llama Community license restricts use above 700M MAU
- Slower and pricier to serve than Llama 3.3 70B at similar quality
Typical workloads
In our catalog grid, Llama 3.1 70B is filed under Reasoning, Pro Writing — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: everyday chat, drafting and summarization.
The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. It ships under the Llama 3 Community license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Dense Transformer · 80 layers · GQA
Training: 15T tokens, Meta multilingual corpus.
A milestone model, but Llama 3.3 70B delivers the same quality with better post-training — use 3.3 unless you have a reason.
Quick start
ollama run llama3.1:70bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Llama 3.1 70B need?
At the recommended Q4_K_M quantization, Llama 3.1 70B needs about 40 GB of VRAM. Q8_0 takes 75 GB, and unquantized FP16 weights take 140 GB.
Can Llama 3.1 70B run without a GPU?
Yes — with roughly 64 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Llama 3.1 70B support?
Llama 3.1 70B supports a 128k-token context window (131,072 tokens).
Can I use Llama 3.1 70B commercially?
Llama 3.1 70B ships under the Llama 3 Community license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Llama 3.1 70B on consumer hardware?
Our compatibility engine estimates on the order of 6 tokens/sec on a mid-range GPU and up to 20 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Llama 3.1 70B should I download first?
Start with Q4_K_M (40 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.