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.
Doesn't fit a 24 GB GPU
Smallest consumer fit → Snapdragon X Elite Copilot+ PC · IQ2_XXS · 25.1 GB · ~4 tok/s
Macs from 32 GB (IQ2_S · ~12 tok/s)
llama3.3
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 3.3 70B Instruct Abliterated
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/bartowski/Llama-3.3-70B-Instruct-abliterated-GGUF:Q4_K_MQ4_K_M needs offload on the RTX 4090 · 24.0 GB short · 16K context
Download: Llama-3.3-70B-Instruct-abliterated-Q4_K_M.gguf42.52 GBbartowski
Other ways
each opens step-by-stepRun in cloud
Fits on A100 80GB (32 GB headroom) · Q4_K_M
- Generation speed
- ~31 tok/s
- generation speed
- Cost per 1M output tokens
- $10.30
- per 1M output tokens
How Much VRAM Does Llama 3.3 70B Instruct Abliterated Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 31.0 GB | 41 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 35.7 GB | |
| 31.8 GB | 41.9 GB | 3-bit small — 3-bit small quantization | Does not fit: needs 36.5 GB | |
| 35.4 GB | 45.4 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 40.1 GB | |
| 36.3 GB | 46.3 GB | 4-bit · legacy — 4-bit legacy quantization | Does not fit: needs 41 GB | |
| 43.3 GB | 53.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 48 GB | |
| 51.2 GB | 61.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 55.9 GB | |
| 59.2 GB | 69.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 63.9 GB | |
| 71.5 GB | 81.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 76.2 GB |
Selected Q4_K_M
4-bit medium quantization48.0GBat 16K context
- Weights
- 42.3 GB
- KV cache
- 5.4 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 24.0 GB more
Which Hardware 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 consumer GPU has enough memory. 67 devices with unified memory can run Llama 3.3 70B Instruct Abliterated, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).
32–40 GB
Q4_K_M · best fit IQ2_M (29.5 GB)
1 GPU · 3 Mac classes · 2 device families
RTX 5090~47Simulate output on RTX 5090M5 Max36 GB~13Simulate output on Mac Studio M5 Max (36 GB)M532 GB~4Simulate output on MacBook Air 13" M5 (32 GB)Orin~5Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~4Simulate output on Snapdragon X Elite Copilot+ PC48 GB
48 GB tier →fits · 0.0 GB free · up to 16K ctx
2 Mac classes · 1 device family
M5 Max48 GB~10Simulate output on Mac Studio M5 Max (48 GB)M5 Pro48 GB~5Simulate output on Mac Mini M5 Pro (48 GB)Snapdragon X2 Elite Extreme (X2E-96-100)~3Simulate output on Snapdragon X2 Elite Extreme Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Llama 3.3 70B Instruct Abliterated
Llama 3 family · 64 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| NVIDIA Nemotron 3.5 Bolt 74B A9B BF16NVIDIA | — | 54.2 GB | —needs > 24 GB | same size |
| Qwen2.5 VL 72B InstructAlibaba | — | 49.7 GB | —needs > 24 GB | same size |
| Qwen2 VL 72B InstructAlibaba | — | 49.7 GB | —needs > 24 GB | same size |
| Qwen1.5 72B ChatAlibaba | — | 86.6 GB | —needs > 24 GB | same size |
| CodeLlama 70B Instruct HFMeta | — | 23.2 GB | ~29 | same size |
| StableBeluga2Stability AI | — | 23.5 GB | ~29 | same size |
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 43.3 GB (at full 131K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 3.3 70B Instruct Abliterated on a Mac?
Yes — Mac Studio M1 Max (32 GB) and 84 other Macs can run Llama 3.3 70B Instruct Abliterated, the smallest at IQ2_S. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 43.3 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M5 Max (48 GB).
Can I run Llama 3.3 70B Instruct Abliterated locally?
Yes — Llama 3.3 70B Instruct Abliterated can run locally on consumer hardware. 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 ~111 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)
Real-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?
67 devices with unified memory can run Llama 3.3 70B Instruct Abliterated at Q4_K_M (43.3 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.