Llama 3.3 70B Instruct Abliterated — Hardware Requirements & GPU Compatibility
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Llama 3.3 70B Instruct Abliterated is a 70.6B-parameter open language model from huihui-ai in the Llama 3 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 43.30 GB of VRAM — see which GPUs and Macs can run it below.
4.3K downloads
74 likesSpecifications
- Publisher
- huihui-ai
- Family
- Llama 3
- Parameters
- 70.6B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-12-12
- License
- llama3.3
Get Started
HuggingFace
How Much VRAM Does Llama 3.3 70B Instruct Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 31.0 GB | 73.2 GB | 29.99 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 31.8 GB | 74.1 GB | 30.87 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 35.4 GB | 77.6 GB | 34.39 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 36.3 GB | 78.5 GB | 35.28 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 43.3 GB | 85.6 GB | 42.33 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 51.2 GB | 93.5 GB | 50.27 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 59.2 GB | 101.5 GB | 58.21 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 71.5 GB | 113.8 GB | 70.55 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama 3.3 70B Instruct Abliterated?
Q4_K_M · 43.3 GBLlama 3.3 70B Instruct Abliterated (Q4_K_M) requires 43.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 57+ GB is recommended. Using the full 131K context window can add up to 42.3 GB, bringing total usage to 85.6 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Llama 3.3 70B Instruct Abliterated?
Q4_K_M · 43.3 GB11 devices with unified memory can run Llama 3.3 70B Instruct Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Related Models
Derivatives (3)
Frequently Asked Questions
- How much VRAM does Llama 3.3 70B Instruct Abliterated need?
Llama 3.3 70B Instruct Abliterated requires 43.3 GB of VRAM at Q4_K_M, or 71.5 GB at Q8_0. Full 131K context adds up to 42.3 GB (85.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 70.6B × 4.8 bits ÷ 8 = 42.3 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 43.3 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M43.3 GBQ4_K_M + full context85.6 GB- Can NVIDIA GeForce RTX 4090 run Llama 3.3 70B Instruct Abliterated?
Yes, at IQ2_S (23.0 GB) or lower. Higher quantizations like IQ2_M (24.8 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Llama 3.3 70B Instruct Abliterated?
For Llama 3.3 70B Instruct Abliterated, Q4_K_M (43.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (44.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 20.4 GB.
VRAM requirement by quantization
IQ2_XXS20.4 GBIQ3_XS30.1 GBQ4_036.3 GBQ4_K_M ★43.3 GBQ4_K_L44.2 GBQ8_071.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.3 70B Instruct Abliterated on a Mac?
Llama 3.3 70B Instruct Abliterated requires at least 20.4 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 Abliterated locally?
Yes — Llama 3.3 70B Instruct Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 43.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.3 70B Instruct Abliterated?
At Q4_K_M, Llama 3.3 70B Instruct Abliterated can reach ~67 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 ÷ 43.3 × 0.55 = ~67 tok/s
Estimated speed at Q4_K_M (43.3 GB)
~67 tok/s~50 tok/s~42 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 Abliterated?
At Q4_K_M, the download is about 42.33 GB. The full-precision Q8_0 version is 70.55 GB. The smallest option (IQ2_XXS) is 19.40 GB.
- Which GPUs can run Llama 3.3 70B Instruct Abliterated?
No single consumer GPU has enough VRAM to run Llama 3.3 70B Instruct Abliterated at Q4_K_M (43.3 GB). Multi-GPU or professional hardware is required.
- Which devices can run Llama 3.3 70B Instruct Abliterated?
11 devices with unified memory can run Llama 3.3 70B Instruct Abliterated at Q4_K_M (43.3 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.