Meta Llama 3 70B Instruct — Hardware Requirements & GPU Compatibility
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Meta Llama 3 70B Instruct is a 70.6B-parameter open language model from Nous Research in the Llama 3 family. It supports a context window of up to 8,192 tokens. At Q4_K_M it needs about 43.30 GB of VRAM — see which GPUs and Macs can run it below.
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Meta Llama 3 70B Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/mradermacher/Meta-Llama-3-70B-Instruct-i1-GGUF:Q4_K_MQ4_K_M needs offload on the RTX 4090 · 21.3 GB short · 8K context
Download: Meta-Llama-3-70B-Instruct.i1-Q4_K_M.gguf42.52 GBmradermacher
Other ways
each opens step-by-stepRun in cloud
Fits on A100 80GB (34 GB headroom) · Q4_K_M
- Generation speed
- ~31 tok/s
- generation speed
- Cost per 1M output tokens
- $11.23
- per 1M output tokens
How Much VRAM Does Meta Llama 3 70B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 31.0 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 31 GB | |
| 31.8 GB | — | 3-bit small — 3-bit small quantization | Does not fit: needs 31.8 GB | |
| 35.4 GB | — | 3-bit medium — 3-bit medium quantization | Does not fit: needs 35.4 GB | |
| 36.3 GB | — | 4-bit · legacy — 4-bit legacy quantization | Does not fit: needs 36.3 GB | |
| 43.3 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 43.3 GB | |
| 51.2 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 51.2 GB | |
| 59.2 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 59.2 GB | |
| 71.5 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 71.5 GB |
Selected Q4_K_M
4-bit medium quantization45.3GBat 8K context
- Weights
- 42.3 GB
- KV cache
- 2.7 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 21.3 GB more, IQ2_XXS fits
Which Hardware Can Run Meta Llama 3 70B Instruct?
Q4_K_M · 43.3 GBMeta Llama 3 70B Instruct (Q4_K_M) requires 43.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 57+ GB is recommended. Using the full 8K context window can add up to 2.0 GB, bringing total usage to 45.3 GB. No consumer GPU has enough memory. 70 devices with unified memory can run Meta Llama 3 70B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Pro M2 Ultra (64 GB).
≤ 20 GB
≤ 20 GB tier →doesn't fit · smallest quant needs 22.4 GB
32–40 GB
Q4_K_M · best fit IQ3_XXS (30.3 GB)
1 GPU · 3 Mac classes · 1 device family
RTX 5090~41Simulate output on RTX 5090M5 Max36 GB~11Simulate 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 32GB48 GB
48 GB tier →fits · 2.7 GB free · up to 8K ctx
2 Mac classes · 1 device family
M5 Max48 GB~10Simulate output on Mac Studio M5 Max (48 GB)M5 Pro48 GB~5Simulate output on Mac Mini M5 Pro (48 GB)Snapdragon X2 Elite Extreme (X2E-96-100)~3Simulate output on Snapdragon X2 Elite Extreme Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Meta Llama 3 70B Instruct
Llama 3 family · 64 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16NVIDIA | — | 54.5 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 |
| CodeLlama 70B Instruct HFMeta | — | 23.2 GB | ~29 | same size |
| StableBeluga2Stability AI | — | 23.5 GB | ~29 | same size |
Specifications
- Publisher
- Nous Research
- Family
- Llama 3
- Parameters
- 70.6B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-04-19
- License
- Other
Frequently Asked Questions
How much VRAM does Meta Llama 3 70B Instruct need?
Meta Llama 3 70B Instruct requires 43.3 GB of VRAM at Q4_K_M, or 142.1 GB at BF16. Full 8K context adds up to 2.0 GB (45.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 70.6B × 4.8 bits ÷ 8 = 42.3 GB
KV Cache + Overhead ≈ 1 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.
KV Cache + Overhead ≈ 3 GB (at full 8K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Meta Llama 3 70B Instruct?
Yes, at IQ2_S (23.0 GB) or lower. Higher quantizations like IQ2_M (24.8 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Meta Llama 3 70B Instruct?
For Meta Llama 3 70B Instruct, Q4_K_M (43.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (49.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 20.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Meta Llama 3 70B Instruct on a Mac?
Yes, but only at lower quantizations. The smallest Mac that can run Meta Llama 3 70B Instruct is Mac Mini M2 (24 GB) at IQ2_XXS; 107 of the 156 Macs we list can run it at some quantization. For Q4_K_M (43.3 GB) you need a Mac with more unified memory.
Can I run Meta Llama 3 70B Instruct locally?
Yes — Meta Llama 3 70B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Meta Llama 3 70B Instruct?
At Q4_K_M, Meta Llama 3 70B Instruct can reach ~111 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 ÷ 43.3 × 0.65 = ~120 tok/s
Estimated speed at Q4_K_M (43.3 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 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 70B Instruct?
No single consumer GPU has enough VRAM to run Meta Llama 3 70B Instruct at Q4_K_M (43.3 GB). Multi-GPU or professional hardware is required.
Which devices can run Meta Llama 3 70B Instruct?
70 devices with unified memory can run Meta Llama 3 70B Instruct at Q4_K_M (43.3 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.