Llama 2 70B Chat — Hardware Requirements & GPU Compatibility
Chat
Llama 2 70B Chat is a 70B-parameter open language model from Meta in the Llama 2 family. At Q4_K_M it needs about 46.20 GB of VRAM — see which GPUs and Macs can run it below.
2 downloads
400 likesSpecifications
- Publisher
- Meta
- Family
- Llama 2
- Parameters
- 70B
- Release Date
- 2024-04-17
- License
- Llama 2 Community
Get Started
HuggingFace
How Much VRAM Does Llama 2 70B Chat Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 32.7 GB | — | 29.75 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 33.7 GB | — | 30.63 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 37.5 GB | — | 34.13 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 38.5 GB | — | 35.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 46.2 GB | — | 42.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 54.9 GB | — | 49.88 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 63.5 GB | — | 57.75 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 77 GB | — | 70.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama 2 70B Chat?
Q4_K_M · 46.2 GBLlama 2 70B Chat (Q4_K_M) requires 46.2 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 2 70B Chat?
Q4_K_M · 46.2 GB11 devices with unified memory can run Llama 2 70B Chat, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Benchmarks
View all 3 →Related Models
Frequently Asked Questions
- How much VRAM does Llama 2 70B Chat need?
Llama 2 70B Chat requires 46.2 GB of VRAM at Q4_K_M, or 77 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 70B × 4.8 bits ÷ 8 = 42 GB
KV Cache + Overhead ≈ 4.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M46.2 GB- Can NVIDIA GeForce RTX 5090 run Llama 2 70B Chat?
Yes, at IQ3_XS (31.8 GB) or lower. Higher quantizations like IQ3_S (32.7 GB) exceed the NVIDIA GeForce RTX 5090's 32 GB.
- What's the best quantization for Llama 2 70B Chat?
For Llama 2 70B Chat, Q4_K_M (46.2 GB) offers the best balance of quality and VRAM usage. Q5_0 (48.1 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 31.8 GB.
VRAM requirement by quantization
IQ3_XS31.8 GBQ3_K_M37.5 GBQ4_K_S43.3 GBQ4_K_M ★46.2 GBQ5_152.9 GBQ8_077.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 2 70B Chat on a Mac?
Llama 2 70B Chat requires at least 31.8 GB at IQ3_XS, 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 2 70B Chat locally?
Yes — Llama 2 70B Chat can run locally on consumer hardware. At Q4_K_M quantization it needs 46.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 2 70B Chat?
At Q4_K_M, Llama 2 70B Chat can reach ~63 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 ÷ 46.2 × 0.55 = ~63 tok/s
Estimated speed at Q4_K_M (46.2 GB)
~63 tok/s~47 tok/s~39 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 2 70B Chat?
At Q4_K_M, the download is about 42.00 GB. The full-precision Q8_0 version is 70.00 GB. The smallest option (IQ3_XS) is 28.88 GB.
- Which GPUs can run Llama 2 70B Chat?
No single consumer GPU has enough VRAM to run Llama 2 70B Chat at Q4_K_M (46.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run Llama 2 70B Chat?
11 devices with unified memory can run Llama 2 70B Chat at Q4_K_M (46.2 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.