Meta Llama 3 70B Instruct Abliterated V3.5 — Hardware Requirements & GPU Compatibility
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Meta Llama 3 70B Instruct Abliterated V3.5 is a 70.6B-parameter open language model from failspy in the Llama 3 family. It supports a context window of up to 8,192 tokens. At BF16 it needs about 142.08 GB of VRAM — see which GPUs and Macs can run it below.
8.6K downloads
53 likesSpecifications
- Publisher
- failspy
- Family
- Llama 3
- Parameters
- 70.6B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-05-30
- License
- Llama 3 Community
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How Much VRAM Does Meta Llama 3 70B Instruct Abliterated V3.5 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 142.1 GB | 144.1 GB | 141.11 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Meta Llama 3 70B Instruct Abliterated V3.5?
BF16 · 142.1 GBMeta Llama 3 70B Instruct Abliterated V3.5 (BF16) requires 142.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 185+ GB is recommended. Using the full 8K context window can add up to 2.0 GB, bringing total usage to 144.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Meta Llama 3 70B Instruct Abliterated V3.5?
BF16 · 142.1 GB4 devices with unified memory can run Meta Llama 3 70B Instruct Abliterated V3.5, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Pro M2 Ultra (192 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Meta Llama 3 70B Instruct Abliterated V3.5 need?
Meta Llama 3 70B Instruct Abliterated V3.5 requires 142.1 GB of VRAM at BF16. Full 8K context adds up to 2.0 GB (144.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 70.6B × 16 bits ÷ 8 = 141.1 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 3 GB (at full 8K context)
VRAM usage by quantization
BF16142.1 GBBF16 + full context144.1 GB- Can NVIDIA GeForce RTX 5090 run Meta Llama 3 70B Instruct Abliterated V3.5?
No — Meta Llama 3 70B Instruct Abliterated V3.5 requires at least 142.1 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Meta Llama 3 70B Instruct Abliterated V3.5 on a Mac?
Meta Llama 3 70B Instruct Abliterated V3.5 requires at least 142.1 GB at BF16, 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 Meta Llama 3 70B Instruct Abliterated V3.5 locally?
Yes — Meta Llama 3 70B Instruct Abliterated V3.5 can run locally on consumer hardware. At BF16 quantization it needs 142.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Meta Llama 3 70B Instruct Abliterated V3.5?
At BF16, Meta Llama 3 70B Instruct Abliterated V3.5 can reach ~21 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 ÷ 142.1 × 0.55 = ~21 tok/s
Estimated speed at BF16 (142.1 GB)
~21 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Meta Llama 3 70B Instruct Abliterated V3.5?
At BF16, the download is about 141.11 GB.
- Which GPUs can run Meta Llama 3 70B Instruct Abliterated V3.5?
No single consumer GPU has enough VRAM to run Meta Llama 3 70B Instruct Abliterated V3.5 at BF16 (142.1 GB). Multi-GPU or professional hardware is required.
- Which devices can run Meta Llama 3 70B Instruct Abliterated V3.5?
4 devices with unified memory can run Meta Llama 3 70B Instruct Abliterated V3.5 at BF16 (142.1 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.