Llama 3.3 70B Instruct — Hardware Requirements & GPU Compatibility
Chat
Meta Llama 3.3 70B Instruct is a 70-billion parameter large language model from Meta, released as part of the Llama 3.3 generation. It is an instruction-tuned model optimized for dialogue and chat use cases, offering strong performance across reasoning, coding, and multilingual tasks. Llama 3.3 70B delivers quality competitive with much larger models while remaining feasible to run on high-end consumer or workstation GPUs with sufficient VRAM. The model uses a grouped-query attention architecture with a 128K token context window and was trained on a massive multilingual corpus. It is released under the Llama 3.3 Community License, making it one of the most capable openly available models for local inference.
596.1K downloads
2.7K likesSpecifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 70B
- Context Length
- 131,072 tokens
- Release Date
- 2024-12-21
- License
- llama3.3
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HuggingFace
How Much VRAM Does Llama 3.3 70B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 21.2 GB | — | 19.25 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_XS | 2.40 | 23.1 GB | — | 21.00 GB | Importance-weighted 2-bit, extra small |
| IQ2_S | 2.50 | 24.1 GB | — | 21.88 GB | Importance-weighted 2-bit, small |
| IQ2_M | 2.70 | 26.0 GB | — | 23.63 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 29.8 GB | — | 27.13 GB | Importance-weighted 3-bit |
| IQ3_XS | 3.30 | 31.8 GB | — | 28.88 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 32.7 GB | — | 29.75 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 33.7 GB | — | 30.63 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 34.6 GB | — | 31.50 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 37.5 GB | — | 34.13 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 38.5 GB | — | 35.00 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 39.5 GB | — | 35.88 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 41.4 GB | — | 37.63 GB | Importance-weighted 4-bit, compact |
| Q4_1 | 4.50 | 43.3 GB | — | 39.38 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 43.3 GB | — | 39.38 GB | 4-bit small quantization |
| IQ4_NL | 4.50 | 43.3 GB | — | 39.38 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 46.2 GB | — | 42.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 47.2 GB | — | 42.88 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 52.9 GB | — | 48.13 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 54.9 GB | — | 49.88 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 55.8 GB | — | 50.75 GB | 5-bit large quantization |
| Q6_K | 6.60 | 63.5 GB | — | 57.75 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 77 GB | — | 70.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama 3.3 70B Instruct?
Q4_K_M · 46.2 GBLlama 3.3 70B Instruct (Q4_K_M) requires 46.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 61+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Llama 3.3 70B Instruct?
Q4_K_M · 46.2 GB11 devices with unified memory can run Llama 3.3 70B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Related Models
Derivatives (5)
Frequently Asked Questions
- How much VRAM does Llama 3.3 70B Instruct need?
Llama 3.3 70B Instruct requires 46.2 GB of VRAM at Q4_K_M, or 77 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 70B × 4.8 bits ÷ 8 = 42 GB
KV Cache + Overhead ≈ 4.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M46.2 GB- Can NVIDIA GeForce RTX 4090 run Llama 3.3 70B Instruct?
Yes, at IQ2_XS (23.1 GB) or lower. Higher quantizations like IQ2_S (24.1 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Llama 3.3 70B Instruct?
For Llama 3.3 70B Instruct, Q4_K_M (46.2 GB) offers the best balance of quality and VRAM usage. Q4_K_L (47.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 21.2 GB.
VRAM requirement by quantization
IQ2_XXS21.2 GB~53%Q2_K32.7 GB~75%Q3_K_L39.5 GB~86%Q4_K_M ★46.2 GB~89%Q4_K_L47.2 GB~90%Q8_077.0 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.3 70B Instruct on a Mac?
Llama 3.3 70B Instruct requires at least 21.2 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Llama 3.3 70B Instruct locally?
Yes — Llama 3.3 70B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 46.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.3 70B Instruct?
At Q4_K_M, Llama 3.3 70B Instruct can reach ~63 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 46.2 × 0.55 = ~63 tok/s
Estimated speed at Q4_K_M (46.2 GB)
AMD Instinct MI300X~63 tok/sNVIDIA H100 SXM~47 tok/sAMD Instinct MI250X~39 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 3.3 70B Instruct?
At Q4_K_M, the download is about 42.00 GB. The full-precision Q8_0 version is 70.00 GB. The smallest option (IQ2_XXS) is 19.25 GB.