Llama 3.1 70B — Hardware Requirements & GPU Compatibility
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Meta Llama 3.1 70B is a 70.6-billion parameter base (pretrained) model from the Llama 3.1 family. It supports a 128K token context window and was trained on a massive multilingual corpus. As a base model, it is designed for fine-tuning and research rather than direct conversational use. The model serves as the foundation for the Llama 3.1 70B Instruct variant and numerous community fine-tunes. It delivers strong performance across language understanding and generation benchmarks. Released under the Llama 3.1 Community License.
Llama 3.1 70B runs on a 64 GB+ Mac or a workstation GPU
Best local pick → Mac Pro M2 Ultra (64 GB) · Q4_K_M · 51.3 GB · ~12 tok/s
Macs from 48 GB (Q3_K_M · ~11 tok/s)
Llama 3.1 Community
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 3.1 70B
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/Llama-3.1-70B --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 H100 (80 GB) (28 GB headroom) · Q4_K_M
- Generation speed
- ~47 tok/s
- generation speed
- Cost per 1M output tokens
- $15.97
- per 1M output tokens
How Much VRAM Does Llama 3.1 70B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 33.0 GB | 43.1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 37.7 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 | |
| 155.2 GB | 165.3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 159.9 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 Llama 3.1 70B?
Q4_K_M · 46.6 GBLlama 3.1 70B (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 Llama 3.1 70B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).
64–80 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
How good is Llama 3.1 70B?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
- MMLU-Pro
TIGER-LabIndependent
Independent52.5%
#71/146 open
Alternatives to Llama 3.1 70B
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(2)
Specifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 70.6B
- Release Date
- 2024-07-14
- License
- Llama 3.1 Community
Frequently Asked Questions
How much VRAM does Llama 3.1 70B need?
Llama 3.1 70B 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 5090 run Llama 3.1 70B?
No — Llama 3.1 70B 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?
For Llama 3.1 70B, 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
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 3.1 70B on a Mac?
Yes — Mac Studio M5 Max (48 GB) and 55 other Macs can run Llama 3.1 70B, the smallest at Q3_K_M. 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 Llama 3.1 70B locally?
Yes — Llama 3.1 70B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 3.1 70B?
At Q4_K_M, Llama 3.1 70B 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 Llama 3.1 70B?
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?
No single consumer GPU has enough VRAM to run Llama 3.1 70B at Q4_K_M (46.6 GB). Multi-GPU or professional hardware is required.
Which devices can run Llama 3.1 70B?
52 devices with unified memory can run Llama 3.1 70B 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.