Meta Llama 3.1 70B Instruct — Hardware Requirements & GPU Compatibility
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Meta Llama 3.1 70B Instruct is a 70.6B-parameter open language model from Meta in the Llama 3 family. At Q4_K_M it needs about 46.57 GB of VRAM — see which GPUs and Macs can run it below.
Doesn't fit a 24 GB GPU
Smallest consumer fit → Mac Studio M1 Max (32 GB) · IQ2_XS · 28 GB · ~12 tok/s
Macs from 32 GB (IQ2_XS · ~12 tok/s)
Llama 3.1 Community
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Meta Llama 3.1 70B Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve meta-llama/Meta-Llama-3.1-70B-Instruct --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on A100 80GB (28 GB headroom) · Q4_K_M
- Generation speed
- ~29 tok/s
- generation speed
- Cost per 1M output tokens
- $11.07
- per 1M output tokens
How Much VRAM Does Meta Llama 3.1 70B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 21.3 GB | 31.5 GB | 2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss | Fits: 8K context · ~31 tok/s | |
| 33.0 GB | 43.1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 37.7 GB | |
| 34.0 GB | 44.1 GB | 3-bit small — 3-bit small quantization | Does not fit: needs 38.7 GB | |
| 37.8 GB | 48 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 42.6 GB | |
| 46.6 GB | 56.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 51.3 GB | |
| 55.3 GB | 65.4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 60 GB | |
| 64.0 GB | 74.2 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 68.8 GB | |
| 77.6 GB | 87.7 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 82.3 GB |
Selected Q4_K_M
4-bit medium quantization51.3GBat 16K context
- Weights
- 45.6 GB
- KV cache
- 5.4 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 27.3 GB more
Or .
Which Hardware Can Run Meta Llama 3.1 70B Instruct?
Q4_K_M · 46.6 GBMeta Llama 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 consumer GPU has enough memory. 52 devices with unified memory can run Meta Llama 3.1 70B Instruct, 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 (30.9 GB)
1 GPU · 3 Mac classes · 1 device family
RTX 5090~45Simulate output on RTX 5090M5 Max36 GB~12Simulate 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 32GB64–80 GB
fits · 12.7 GB free · up to 32K ctx
3 Mac classes · 1 device family
M2 Ultra*64 GB~12Simulate output on Mac Pro M2 Ultra (64 GB)M5 Max64 GB~9Simulate output on Mac Studio M5 Max (64 GB)Orin~3Simulate output on NVIDIA Jetson AGX Orin 64GB
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Meta Llama 3.1 70B Instruct
Llama 3 family · 65 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 |
| StableBeluga2Stability AI | — | 23.5 GB | ~29 | same size |
| Apertus 70B 2509swiss-ai | — | 48 GB | —needs > 24 GB | same size |
Fine-tunes & variants(1)
Specifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 70.6B
- Release Date
- 2024-07-16
- License
- Llama 3.1 Community
Frequently Asked Questions
How much VRAM does Meta Llama 3.1 70B Instruct need?
Meta 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Meta Llama 3.1 70B Instruct?
Yes, at IQ2_XS (23.3 GB) or lower. Higher quantizations like IQ2_S (24.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Meta Llama 3.1 70B Instruct?
For Meta Llama 3.1 70B Instruct, Q4_K_M (46.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (47.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 21.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Meta Llama 3.1 70B Instruct on a Mac?
Yes — Mac Studio M1 Max (32 GB) and 84 other Macs can run Meta Llama 3.1 70B Instruct, the smallest at IQ2_XS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 46.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Pro M2 Ultra (64 GB).
Can I run Meta Llama 3.1 70B Instruct locally?
Yes — Meta Llama 3.1 70B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Meta Llama 3.1 70B Instruct?
At Q4_K_M, Meta Llama 3.1 70B Instruct can reach ~103 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)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Meta 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 (IQ2_XXS) is 19.40 GB.
Which GPUs can run Meta Llama 3.1 70B Instruct?
No single consumer GPU has enough VRAM to run Meta Llama 3.1 70B Instruct at Q4_K_M (46.6 GB). Multi-GPU or professional hardware is required.
Which devices can run Meta Llama 3.1 70B Instruct?
52 devices with unified memory can run Meta Llama 3.1 70B Instruct at Q4_K_M (46.6 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.