LLMLocal inference
Qwen2.5 72B
Weight estimates and planning VRAM for running Qwen2.5 72B locally at each quantization level. Compare the lowest reference-cost devices that clear the plan.
Q4 plan
51 GB
Q8 plan
90 GB
FP16 plan
167 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 Qwen GGUF model card ↗
01 // GPUs that can run Qwen2.5 72B
Compatible hardware by quantization
Sorted by dated reference-cost estimates from Apr 30, 2026. These are not live offers.
Q4Q4_K_M (4-bit)
44GB weights · plan ≥51GBGPUVRAMPriceTier
Q8Q8_0 (8-bit)
77.5GB weights · plan ≥90GBGPUVRAMPriceTier
FP16FP16 (full precision)
145GB weights · plan ≥167GBGPUVRAMPriceTier
02 // Frequently asked
Qwen2.5 72B GPU questions
How much VRAM does Qwen2.5 72B need?
Qwen2.5 72B uses approximately 44GB for Q4 weights, 77.5GB at Q8, or 145GB at FP16. GPU Hunter adds 15% planning headroom for runtime buffers and a modest KV cache, producing targets of 51GB, 90GB, and 167GB respectively. Exact memory use varies by backend and context length.
What is the cheapest GPU to run Qwen2.5 72B?
Using GPU Hunter's 51GB Q4 planning target, the lowest reference-cost single device is the NVIDIA DGX Spark (128GB VRAM, dated estimate $3,999).
Can I run Qwen2.5 72B at FP16?
Qwen2.5 72B uses about 145GB for FP16 weights and 167GB under GPU Hunter's planning allowance—well beyond a single consumer GPU. Q4 or Q8 is more practical.
What quantization is best for Qwen2.5 72B?
Q4_K_M uses about 44GB for weights and is the most hardware-accessible option. Q8_0 uses about 77.5GB and trades more memory for fidelity. FP16 uses about 145GB before runtime and context overhead. The right choice depends on the task, backend, and context window.