Meta Llama 3 70B — Hardware Requirements & GPU Compatibility
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Meta Llama 3 70B 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.
Meta Llama 3 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 Community
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Meta Llama 3 70B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve meta-llama/Meta-Llama-3-70B --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on A100 80GB (28 GB headroom) · Q4_K_M
- Generation speed
- ~29 tok/s
- generation speed
- Cost per 1M output tokens
- $13.55
- per 1M output tokens
How Much VRAM Does Meta Llama 3 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
Which Hardware Can Run Meta Llama 3 70B?
Q4_K_M · 46.6 GBMeta Llama 3 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 Meta Llama 3 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 Meta Llama 3 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
Here are all 3 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- MMLU-Pro
TIGER-LabIndependent
Independent52.8%
#69/145 open
- ForecastBench
Epoch AI · Apr 2024Lab-reported
Lab-reported57.1
#19/26 open
- WinoGrande
Epoch AI · Apr 2024Lab-reported
Lab-reported83.5%
#5/51 open
Alternatives to Meta Llama 3 70B
Llama 3 family · 64 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 |
Specifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 70.6B
- Release Date
- 2024-04-17
- License
- Llama 3 Community
Frequently Asked Questions
How much VRAM does Meta Llama 3 70B need?
Meta Llama 3 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 Meta Llama 3 70B?
No — Meta Llama 3 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 Meta Llama 3 70B?
For Meta Llama 3 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 Meta Llama 3 70B on a Mac?
Yes — Mac Studio M5 Max (48 GB) and 55 other Macs can run Meta Llama 3 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 Meta Llama 3 70B locally?
Yes — Meta Llama 3 70B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Meta Llama 3 70B?
At Q4_K_M, Meta Llama 3 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 Meta Llama 3 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 Meta Llama 3 70B?
No single consumer GPU has enough VRAM to run Meta Llama 3 70B at Q4_K_M (46.6 GB). Multi-GPU or professional hardware is required.
Which devices can run Meta Llama 3 70B?
52 devices with unified memory can run Meta Llama 3 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.