LLMLocal inference
Llama 3.1 8B
Weight estimates and planning VRAM for running Llama 3.1 8B locally at each quantization level. Compare the lowest reference-cost devices that clear the plan.
Q4 plan
6 GB
Q8 plan
11 GB
FP16 plan
19 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 3.1 8B
Compatible hardware by quantization
Sorted by dated reference-cost estimates from Apr 30, 2026. These are not live offers.
Q4Q4_K_M (4-bit)
5GB weights · plan ≥6GBGPUVRAMPriceTier
Q8Q8_0 (8-bit)
9GB weights · plan ≥11GBGPUVRAMPriceTier
FP16FP16 (full precision)
16GB weights · plan ≥19GBGPUVRAMPriceTier
02 // Frequently asked
Llama 3.1 8B GPU questions
How much VRAM does Llama 3.1 8B need?
Llama 3.1 8B uses approximately 5GB for Q4 weights, 9GB at Q8, or 16GB at FP16. GPU Hunter adds 15% planning headroom for runtime buffers and a modest KV cache, producing targets of 6GB, 11GB, and 19GB respectively. Exact memory use varies by backend and context length.
What is the cheapest GPU to run Llama 3.1 8B?
Using GPU Hunter's 6GB Q4 planning target, the lowest reference-cost single device is the GeForce RTX 3060 12GB (12GB VRAM, dated estimate $249).
Can I run Llama 3.1 8B at FP16?
Potentially. Llama 3.1 8B uses about 16GB for FP16 weights and 19GB under GPU Hunter's planning allowance. Confirm the backend and context requirement before purchasing.
What quantization is best for Llama 3.1 8B?
Q4_K_M uses about 5GB for weights and is the most hardware-accessible option. Q8_0 uses about 9GB and trades more memory for fidelity. FP16 uses about 16GB before runtime and context overhead. The right choice depends on the task, backend, and context window.