What is Llama 3.3 70B and how does it differ from Llama 3.1 70B?

Llama 3.3 70B is Meta's latest 70-billion parameter open-weight model, released in December 2024. It improves on Llama 3.1 70B with better instruction following (86.0% vs 79.3% MMLU), stronger coding ability (81.7% vs 72.6% HumanEval), improved math reasoning (91.1% vs 83.7% GSM8K), and reduced hallucination. The architecture is similar (dense transformer, 128K context), but training data and alignment techniques are improved. It uses the same Llama Community License.

How much VRAM do I need to run Llama 3.3 70B?

With Q4_K_M quantization (Ollama default): ~40GB VRAM. Options: RTX 5090 (32GB) with some CPU offload, dual RTX 4090 (48GB total), Mac M4 Max with 64GB unified memory, or a cloud GPU like A100 80GB. With Q2_K quantization you can fit it in ~27GB but quality drops noticeably. For the best experience, 48GB+ of VRAM or Apple Silicon unified memory is recommended.

How do I install Llama 3.3 70B with Ollama?

Run: ollama pull llama3.3:70b (downloads ~40GB). Then: ollama run llama3.3:70b to start chatting. Ollama automatically uses Q4_K_M quantization and GPU acceleration. On Apple Silicon, unified memory is used seamlessly. On NVIDIA, ensure you have recent drivers (535+). The download takes 10-30 minutes depending on your connection speed.

Is Llama 3.3 70B better than GPT-4?

Llama 3.3 70B matches or exceeds GPT-4-turbo on several benchmarks: MMLU (86.0% vs ~86.4%), GSM8K math (91.1% vs ~92%), coding (81.7% vs ~67% HumanEval). However, GPT-4o remains stronger on complex reasoning, creative writing, and multi-turn conversations. Llama 3.3 70B's key advantage is that it runs locally for free with full privacy. For most practical tasks (coding, Q&A, summarization, analysis), Llama 3.3 70B is a credible local alternative.

Can I run Llama 3.3 70B on a Mac?

Yes, on Apple Silicon Macs with sufficient unified memory. M3 Max or M4 Max with 64GB handles Llama 3.3 70B (Q4_K_M) at ~18 tok/s. M2 Ultra 128GB runs it comfortably at ~15 tok/s. M1/M2 Pro/Max with 32GB cannot run the 70B version — use Llama 3.3 8B instead (when available) or Qwen 2.5 32B. Ollama handles Apple Silicon GPU acceleration automatically.

What is the context window of Llama 3.3 70B?

Llama 3.3 70B supports a 128K token context window (~200 pages of text). In practice, Ollama defaults to a smaller context (typically 2048-4096 tokens) for faster inference. To use the full context, create a Modelfile with PARAMETER num_ctx 32768 or higher, or use /set parameter num_ctx 32768 in the Ollama chat. Note that larger context windows require more VRAM — 128K context on 70B can require 60GB+ VRAM.

Should I use Llama 3.3 70B or Qwen 2.5 72B?

Both are excellent 70B-class models. Llama 3.3 70B is stronger in English, coding, and math (86.0% MMLU, 81.7% HumanEval). Qwen 2.5 72B is better for multilingual tasks, especially Chinese and other Asian languages. Llama 3.3 uses the Llama Community License (commercial use allowed with restrictions for 700M+ monthly users). Qwen 2.5 uses Apache 2.0 (fully permissive). Choose Llama 3.3 for English-first tasks, Qwen 2.5 if you need multilingual support or a more permissive license.

What quantization should I use for Llama 3.3 70B?

Q4_K_M (Ollama default) is the best balance: ~40GB VRAM, 95-98% of full quality, ~18 tok/s on RTX 4090. Q5_K_M (~48GB) is marginally better quality but needs more VRAM. Q3_K_M (~33GB) fits on RTX 5090 with room to spare but has noticeable quality loss on complex reasoning. Q2_K (~27GB) fits on 32GB GPUs but quality degrades significantly. Avoid going below Q3_K_M for a 70B model.

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Llama 3.3 70B

Meta's most capable open-weight model. 86.0% MMLU, 81.7% HumanEval, 128K context. Runs locally with Ollama on 48GB+ hardware.

MMLU Score

Good

One-Command Install

$ ollama pull llama3.3:70b

# Downloads ~40GB, takes 10-30 min

$ ollama run llama3.3:70b

# Start chatting immediately

VRAM: ~40GB (Q4_K_M)Speed: ~18 tok/s (RTX 4090)License: Llama Community

What's New in Llama 3.3 vs 3.1

Llama 3.3 70B, released December 2024, is Meta's latest instruction-tuned model. It replaces Llama 3.1 70B as the default "big" open-weight model for local and production use.

Metric	Llama 3.1 70B	Llama 3.3 70B	Improvement
MMLU	79.3%	86.0%	+6.7 pts
HumanEval (coding)	72.6%	81.7%	+9.1 pts
GSM8K (math)	83.7%	91.1%	+7.4 pts
MATH	51.9%	68.0%	+16.1 pts
Context Window	128K	128K	Same
VRAM (Q4_K_M)	~40GB	~40GB	Same
Instruction Following	Good	Significantly better	Improved

Key improvements: stronger reasoning, better coding, reduced hallucination, more consistent instruction following. Same hardware requirements as Llama 3.1 70B — a direct upgrade. Scores from official Meta model cards; verify latest figures on Hugging Face.

Technical Specifications

Architecture

Parameters	70.6 billion
Architecture	Dense Transformer (Decoder-only)
Layers	80
Hidden Size	8,192
Attention Heads	64 (8 KV heads, GQA)
Vocabulary	128,256 tokens (tiktoken)
Context Window	128,000 tokens

Training

Developer	Meta AI
Release	December 2024
Training Data	15+ trillion tokens
Data Cutoff	December 2023
Alignment	RLHF + DPO
License	Llama Community License
Commercial Use	Yes (up to 700M monthly users)

Benchmark Performance

Benchmark scores from Meta's official model card. MMLU = general knowledge, HumanEval = Python coding, GSM8K = grade-school math, MATH = competition math, ARC = science reasoning. Speed estimates are community-reported and vary by system configuration.

Quick Start: Run Llama 3.3 70B with Ollama

Step-by-Step

Install Ollama

macOS: brew install ollama | Linux: curl -fsSL https://ollama.com/install.sh | sh | Windows: download from ollama.com

Pull the model (~40GB download)

ollama pull llama3.3:70b

Start chatting

ollama run llama3.3:70b

Optional: Add a web interface

See our Open WebUI setup guide for a ChatGPT-like interface

Hardware Requirements

VRAM by Quantization

Hardware	Quantization	Speed	Experience
RTX 5090 (32GB)	Q3_K_M	~22 tok/s	Good (slight quality loss)
2x RTX 4090 (48GB)	Q4_K_M	~18 tok/s	Excellent
Mac M4 Max 64GB	Q4_K_M	~18 tok/s	Excellent
Mac M2 Ultra 128GB	Q5_K_M	~15 tok/s	Excellent (higher quality)
A100 80GB	Q4_K_M	~35 tok/s	Production-ready
RTX 4090 (24GB) + CPU offload	Q4_K_M (split)	~8 tok/s	Usable but slow

Llama 3.3 70B vs Competitors

vs Cloud APIs (GPT-4o, Claude)

+ Free — no per-token costs
+ Full privacy — data stays local
+ No rate limits or downtime
− Requires expensive hardware
− Slower than cloud for complex tasks
− Weaker on creative writing, nuance

vs Smaller Local Models (8B-32B)

+ Dramatically better reasoning
+ Fewer hallucinations
+ Better at following complex instructions
− 5-8x more VRAM required
− 3-5x slower inference
− Cannot run on most consumer hardware

Best Use Cases for Llama 3.3 70B

Excels At

Code generation — 81.7% HumanEval, strong across Python/JS/Rust
Math & reasoning — 91.1% GSM8K, competitive with o1-mini
Document analysis — 128K context handles long documents
Summarization — Reliable, factual summaries
Enterprise Q&A — Pair with RAG for internal knowledge bases
Multi-turn conversation — Good memory across long chats

Consider Alternatives For

Real-time chat — Use 8B models for faster responses
Multilingual — Qwen 2.5 72B is better for CJK languages
Coding autocomplete — Qwen 2.5 Coder 1.5B is faster for tab completion
Chain-of-thought reasoning — DeepSeek R1 32B shows thinking process
8GB hardware — Use 8B models instead

Quantization Guide

Ollama defaults to Q4_K_M, which is the best balance of quality and memory. Here are your options:

Quantization	Download Size	VRAM	Quality	Ollama Command
Q2_K	~25 GB	~27 GB	Poor (not recommended)	ollama pull llama3.3:70b-instruct-q2_K
Q3_K_M	~31 GB	~33 GB	Acceptable	ollama pull llama3.3:70b-instruct-q3_K_M
Q4_K_M (default)	~40 GB	~42 GB	Excellent (95-98%)	ollama pull llama3.3:70b
Q5_K_M	~46 GB	~48 GB	Near-lossless	ollama pull llama3.3:70b-instruct-q5_K_M
Q8_0	~70 GB	~72 GB	Essentially lossless	ollama pull llama3.3:70b-instruct-q8_0

For a detailed explanation of quantization formats, see our AWQ vs GPTQ vs GGUF comparison.

Advanced Setup

Custom Modelfile

Create a tuned version with custom parameters:

# Save as Modelfile

FROM llama3.3:70b

PARAMETER temperature 0.7

PARAMETER num_ctx 16384

PARAMETER top_p 0.9

SYSTEM "You are a helpful technical assistant. Be concise and accurate."

# Build: ollama create my-llama -f Modelfile

# Run: ollama run my-llama

API Access

Use Ollama's OpenAI-compatible API:

curl http://localhost:11434/v1/chat/completions \

-H "Content-Type: application/json" \

-d '{"model": "llama3.3:70b", "messages": [{"role": "user", "content": "Hello"}]}'

Pair with a Web Interface

For the best experience, pair Llama 3.3 70B with Open WebUI for a full ChatGPT-like interface, or use it as the backend for Continue.dev for AI-assisted coding.

Sources

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Written by Pattanaik Ramswarup

Creator of Local AI Master

I build Local AI Master around practical, testable local AI workflows: model selection, hardware planning, RAG systems, agents, and MLOps. The goal is to turn scattered tutorials into a structured learning path you can follow on your own hardware.

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📅 Published: March 17, 2026🔄 Last Updated: March 17, 2026✓ Manually Reviewed

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