Llama 3.1 70B Instruct — Hardware Requirements & GPU Compatibility
Chat
Meta Llama 3.1 70B Instruct is a 70.6-billion parameter instruction-tuned model from Meta's Llama 3.1 family. It features a 128K token context window and is optimized for chat, tool use, and complex reasoning tasks. The 70B size offers a strong balance between capability and hardware requirements, running well on multi-GPU setups or high-VRAM workstation cards. This model was trained on over 15 trillion tokens and fine-tuned with reinforcement learning from human feedback (RLHF). It excels at coding assistance, mathematical reasoning, and multilingual dialogue. Released under the Llama 3.1 Community License.
630.4K downloads
924 likesSpecifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 70.6B
- Context Length
- 131,072 tokens
- Release Date
- 2024-07-16
- License
- Llama 3.1 Community
Get Started
HuggingFace
How Much VRAM Does Llama 3.1 70B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 33.0 GB | — | 29.99 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 37.8 GB | — | 34.39 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 46.6 GB | — | 42.33 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 55.3 GB | — | 50.27 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 64.0 GB | — | 58.21 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 77.6 GB | — | 70.55 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 155.2 GB | — | 141.11 GB | Brain floating point 16 — preferred for training |
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 3.1 70B Instruct?
Q4_K_M · 46.6 GBLlama 3.1 70B Instruct (Q4_K_M) requires 46.6 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 3.1 70B Instruct?
Q4_K_M · 46.6 GB26 devices with unified memory can run Llama 3.1 70B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Where to Download Llama 3.1 70B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Llama 3.1 70B Instruct need?
Llama 3.1 70B Instruct requires 46.6 GB of VRAM at Q4_K_M, or 155.2 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 70.6B × 4.8 bits ÷ 8 = 42.3 GB
KV Cache + Overhead ≈ 4.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M46.6 GB- Can NVIDIA GeForce RTX 5090 run Llama 3.1 70B Instruct?
No — Llama 3.1 70B Instruct requires at least 33.0 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Llama 3.1 70B Instruct?
For Llama 3.1 70B Instruct, Q4_K_M (46.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (55.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 33.0 GB.
VRAM requirement by quantization
Q2_K33.0 GBQ4_K_M ★46.6 GBQ5_K_M55.3 GBQ6_K64.0 GBQ8_077.6 GBBF16155.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.1 70B Instruct on a Mac?
Llama 3.1 70B Instruct requires at least 33.0 GB at Q2_K, 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 3.1 70B Instruct locally?
Yes — Llama 3.1 70B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 46.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.1 70B Instruct?
At Q4_K_M, Llama 3.1 70B Instruct can reach ~95 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 ÷ 46.6 × 0.65 = ~112 tok/s
Estimated speed at Q4_K_M (46.6 GB)
~112 tok/s~112 tok/s~95 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 3.1 70B Instruct?
At Q4_K_M, the download is about 42.33 GB. The full-precision BF16 version is 141.11 GB. The smallest option (Q2_K) is 29.99 GB.
- Which GPUs can run Llama 3.1 70B Instruct?
No single consumer GPU has enough VRAM to run Llama 3.1 70B Instruct at Q4_K_M (46.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run Llama 3.1 70B Instruct?
27 devices with unified memory can run Llama 3.1 70B Instruct at Q4_K_M (46.6 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.