How to Deploy Llama 3.3 70B Locally
Llama 3.3 70B is a dense model with 70.6B parameters. At Q4 it needs at least 43GB of VRAM, and 52GB is the comfortable amount for an 8K context. The cheapest card that runs it today is the Ryzen AI Max+ 395 128GB at about $3,913, at roughly 3.1 tokens/s.
Llama 3.3 70B details
How much VRAM does Llama 3.3 70B need for local deployment?
| Quantization | WeightsGB | Total at 8KGB | Total at 32KGB | Total at 128KGB |
|---|---|---|---|---|
| 1-bit | 16.8GGUF | 20.1 | 27.6 | 57.6 |
| 2-bit | 27.4GGUF | 29.7 | 37.2 | 67.2 |
| 3-bit | 38.1GGUF | 37.3 | 44.8 | 74.8 |
| 4-bit | 42.5GGUF | 42.8 | 50.3 | 80.3 |
| 5-bit | 49.9GGUF | 51.1 | 58.6 | 88.6 |
| 8-bit | 75GGUF | 76.6 | 84.1 | 114.1 |
| FP16 | 141.1GGUF | 141.5 | 149 | 179 |
Total = weights + KV cache (FP16) + 1GB runtime overhead. Weights marked GGUF are measured file sizes from the repository; the rest are estimated from bytes per parameter. Contexts beyond this model's 128K limit show a dash. Each tier takes whichever file of that bit width the repository has, preferring unsloth dynamic quants, so the exact file name varies by model.
Which GPUs can run Llama 3.3 70B?
AmazonJP
| Device | VRAMGB | Price | Est. t/s | Suggested context |
|---|---|---|---|---|
 Ryzen AI Max+ 395 128GB | 128 | $3,9136 listings | 3.1 | 128K |
 DGX Spark 128GB | 128 | $8,2233 listings | 4.6 | 128K |
| GeForce RTX 4090 48GB | 48 | —No listings | 16.8 | 8K |
| Radeon PRO W7900 | 48 | —No listings | 10.3 | 8K |
| RTX PRO 5000 Blackwell 48GB | 48 | —No listings | 22.3 | 8K |
| CMP 170HX 64GB | 64 | —No listings | 24.7 | 32K |
| Instinct MI210 | 64 | —No listings | 19.4 | 32K |
| RTX PRO 5000 Blackwell 72GB | 72 | —No listings | 22.3 | 32K |
| RTX PRO 6000D | 84 | —No listings | 25.9 | 128K |
| RTX PRO 6000 Blackwell | 96 | —No listings | 29.5 | 128K |
| Mac Studio M4 Max 128GB | 128 | —No listings | 7.2 | 128K |
| Mac Studio M5 Max 128GB | 128 | —No listings | 8.1 | 128K |
| MacBook Pro M4 Max 128GB | 128 | —No listings | 7.2 | 128K |
| MacBook Pro M5 Max 128GB | 128 | —No listings | 8.1 | 128K |
| Mac Studio M3 Ultra 512GB | 512 | —No listings | 10.8 | 128K |
Fits when weights + KV cache + runtime overhead is at or below usable memory, single card. "Offload" on MoE models means it runs with expert weights in system RAM (only attention layers and KV cache stay in VRAM); that speed assumes 70 GB/s RAM bandwidth. The context column is the largest tier that fits.
Deploy Llama 3.3 70B locally with Ollama, llama.cpp or vLLM
Ollama pulls and runs it in one command; the quantization comes from the official tag.
ollama run llama3.3:70b
llama.cpp pulls the GGUF straight from Hugging Face; -c sets the context length.
llama-server -hf bartowski/Llama-3.3-70B-Instruct-GGUF:Q4_K_M -c 8192
vLLM serves the original-precision weights, which needs far more memory than GGUF and usually more than one GPU.
vllm serve meta-llama/Llama-3.3-70B-Instruct
File names follow that week's model-library snapshot; the definitions are in the methodology. Methodology
FAQ
How much VRAM does Llama 3.3 70B need?
At Q4_K_M with an 8K context Llama 3.3 70B needs about 43GB of VRAM; 52GB leaves comfortable headroom.
What is the cheapest GPU that runs Llama 3.3 70B?
The Ryzen AI Max+ 395 128GB: 128GB of VRAM, currently about $3,913, at roughly 3.1 tokens/s.
Can you run Llama 3.3 70B with Ollama?
Yes: ollama run llama3.3:70b.