LLMLocal inference
Llama 3.3 70B
Weight estimates and planning VRAM for running Llama 3.3 70B locally at each quantization level. Compare the lowest reference-cost devices that clear the plan.
Q4 plan
46 GB
Q8 plan
87 GB
FP16 plan
161 GB
Context window
128 k tokens
Planning targets add 15% to weight estimates for runtime buffers and a modest KV cache. Long context or a different backend can require more. Official Meta model card ↗
01 // GPUs that can run Llama 3.3 70B
Compatible hardware by quantization
Sorted by dated reference-cost estimates from Apr 30, 2026. These are not live offers.
Q4Q4_K_M (4-bit)
40GB weights · plan ≥46GBGPUVRAMPriceTier
Q8Q8_0 (8-bit)
75GB weights · plan ≥87GBGPUVRAMPriceTier
FP16FP16 (full precision)
140GB weights · plan ≥161GBGPUVRAMPriceTier
02 // Frequently asked
Llama 3.3 70B GPU questions
How much VRAM does Llama 3.3 70B need?
Llama 3.3 70B uses approximately 40GB for Q4 weights, 75GB at Q8, or 140GB at FP16. GPU Hunter adds 15% planning headroom for runtime buffers and a modest KV cache, producing targets of 46GB, 87GB, and 161GB respectively. Exact memory use varies by backend and context length.
What is the cheapest GPU to run Llama 3.3 70B?
Using GPU Hunter's 46GB Q4 planning target, the lowest reference-cost single device is the Apple M4 Pro (48GB VRAM, dated estimate $2,499).
Can I run Llama 3.3 70B at FP16?
Llama 3.3 70B uses about 140GB for FP16 weights and 161GB under GPU Hunter's planning allowance—well beyond a single consumer GPU. Q4 or Q8 is more practical.
What quantization is best for Llama 3.3 70B?
Q4_K_M uses about 40GB for weights and is the most hardware-accessible option. Q8_0 uses about 75GB and trades more memory for fidelity. FP16 uses about 140GB before runtime and context overhead. The right choice depends on the task, backend, and context window.