LLMLocal inference
Llama 3.3 70B
VRAM requirements to run Llama 3.3 70B locally at each quantization level. Find the cheapest GPU that fits below.
Q4 VRAM
40 GB
Q8 VRAM
75 GB
FP16 VRAM
140 GB
Context window
128 k tokens
01 // GPUs that can run Llama 3.3 70B
Cheapest compatible hardware by quantization
Sorted cheapest first. All prices are approximate street prices.
Q4Q4_K_M (4-bit)
needs ≥40 GB VRAMGPUVRAMPriceTier
Q8Q8_0 (8-bit)
needs ≥75 GB VRAMGPUVRAMPriceTier
FP16FP16 (full precision)
needs ≥140 GB VRAMGPUVRAMPriceTier
02 // Frequently asked
Llama 3.3 70B GPU questions
How much VRAM does Llama 3.3 70B need?
Llama 3.3 70B requires approximately 40GB VRAM at Q4 quantization, 75GB at Q8, or 140GB at full FP16 precision. Q4 is the most practical choice for consumer hardware.
What is the cheapest GPU to run Llama 3.3 70B?
The cheapest single GPU that fits Llama 3.3 70B at Q4 is the Apple M4 Pro (48GB VRAM, ~$2,499). At Q4 you need at least 40GB.
Can I run Llama 3.3 70B at FP16?
Llama 3.3 70B at FP16 requires 140GB VRAM — well beyond any single consumer GPU. FP16 is only practical on multi-GPU server configurations. Q4 (40GB) or Q8 (75GB) are the realistic options.
What quantization is best for Llama 3.3 70B?
Q4_K_M (40GB) offers the best hardware compatibility and still produces high-quality output. Q8_0 (75GB) is better for tasks needing higher accuracy at the cost of needing more VRAM. FP16 (140GB) is only practical on very high-end workstation hardware.