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
- 2024-12-11
- License
- llama3.3
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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 |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
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 GB27 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
Decent
— Enough memory, may be tightWhere to Download Llama 3.3 70B Instruct Abliterated
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
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 142.1 GB at BF16. 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 GBQ3_K_L37.1 GBQ4_K_M ★43.3 GBQ4_K_L44.2 GBBF16142.1 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 ~102 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 43.3 × 0.65 = ~120 tok/s
Estimated speed at Q4_K_M (43.3 GB)
~120 tok/s~120 tok/s~102 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 BF16 version is 141.11 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?
27 devices with unified memory can run Llama 3.3 70B Instruct Abliterated at Q4_K_M (43.3 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.