Llama 3.3 70B Instruct
By Meta · United States
Updated 2026-07-13
Overview
Meta's Llama 3.3 70B — same quality tier as Llama 3.1 405B at one-sixth the size, thanks to improved post-training. Weights are gated on Hugging Face.
When to pick this model
- Self-hosted alternatives to GPT-4 and Claude APIs
- Long-context reasoning and code on multi-GPU servers
- Production workloads where 405B is too expensive to run
- Domain fine-tuning on a high-quality 70B base
- Enterprise deployments cleared under the Llama Community license
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 3.3 70B Instruct 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 3.3 70B Instruct 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 3.3 70B Instruct 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 3.3 70B Instruct |
|---|---|---|
| 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 3.3 70B Instruct?
To run Llama 3.3 70B Instruct 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 Diamond | 50.5 |
| HumanEval | 88.4 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put Llama 3.3 70B Instruct in context: its MMLU score of 86 ranks #5 of the 34 catalog models with a published MMLU result (catalog median 73.4); its HumanEval score of 88.4 ranks #5 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
- Quality competitive with Llama 3.1 405B
- 128k context window
- Strong reasoning and code performance
- Major efficiency gain vs the 405B model
Limitations
- Hugging Face access is gated — must accept Meta's terms
- Llama Community license restricts use above 700M MAU
- No vision capabilities
- Still needs roughly 40GB VRAM at Q4
Typical workloads
In our catalog grid, Llama 3.3 70B Instruct is filed under Reasoning, Pro Writing, Agents — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the Llama 3.3 Community license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Dense · GQA · Llama 3.1 base
Training: Improved post-training vs Llama 3.1 70B.
The best open-weight 70B available — pick it over Llama 3.1 70B unless you have a hard reason not to.
Quick start
ollama run llama3.3:70bOr 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 3.3 70B Instruct need?
At the recommended Q4_K_M quantization, Llama 3.3 70B Instruct needs about 40 GB of VRAM. Q8_0 takes 75 GB, and unquantized FP16 weights take 140 GB.
Can Llama 3.3 70B Instruct 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 3.3 70B Instruct support?
Llama 3.3 70B Instruct supports a 125k-token context window (128,000 tokens).
Can I use Llama 3.3 70B Instruct commercially?
Llama 3.3 70B Instruct ships under the Llama 3.3 Community license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Llama 3.3 70B Instruct 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 3.3 70B Instruct 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.