How much VRAM does Llama 3.3 70B Instruct need?
Llama 3.3 70B Instruct needs about 42.6 GB of VRAM at Q4_K_M, or 74.1 GB at Q8_0 and 138.8 GB at FP16. No consumer card holds it at Q4_K_M. The smallest fit in our set is the RTX A6000 (48 GB). Weights plus runtime overhead. Budget separately for KV cache, which at 128K is a material share.
Weights-plus-runtime footprint across the six most common quantizations. KV cache is context-dependent and comes on top. At 128K it is gigabytes on its own, before the weights; size it precisely in the GPU Memory Calculator.
Can your GPU run Llama 3.3 70B Instruct?
Pick your card — the answer below is computed by the same engine that produces every figure on this page.
A100 80GB runs Llama 3.3 70B Instruct at Q4_K_M with 33.4 GB to spare; comfortable at this size, where most cards cannot hold the weights at all.
Weights plus runtime overhead. KV cache is not included — it depends on context length and this model’s published architecture.
| Weights (Q4_K_M) | 39.9 GB |
| Runtime overhead | 2.7 GB |
| Estimated total | 42.6 GB |
| Usable VRAM (95% of 80 GB) | 76.0 GB |
Across every GPU we track
- RTX 3060 12GBNot enough-31.2 GB
- RTX 4060 Ti 16GBNot enough-27.4 GB
- RTX 4070 Ti SUPERNot enough-27.4 GB
- RTX 3090Not enough-19.8 GB
- RTX 4090Not enough-19.8 GB
- RTX 5090Not enough-12.2 GB
- A100 40GBNot enough-4.6 GB
- RTX A6000Tight+3.0 GB
- L40STight+3.0 GB
- A100 80GBExcellent+33.4 GB
- H100 80GBExcellent+33.4 GB
- H200 141GBExcellent+91.3 GB
Estimates, not guarantees: real usage moves with runtime, driver, batch size and context. Size a specific context window in the GPU Memory Calculator.
How was this number calculated? Every figure here comes from Bitpute’s documented calculation methodology — parameters, precision, quantization, runtime overhead and usable VRAM, each formula written out in full.
Memory by quantization
| Quant | Weights | + runtime overhead |
|---|---|---|
| FP16 | 131.5 GB | 138.8 GB |
| Q8_0 | 69.9 GB | 74.1 GB |
| Q6_K | 53.9 GB | 57.4 GB |
| Q5_K_M | 46.8 GB | 49.9 GB |
| Q4_K_M | 39.9 GB | 42.6 GB |
| Q4_0 | 37.0 GB | 39.6 GB |
Overhead = 0.75 GB + 5% of weights (CUDA context, buffers). Add KV cache on top. Weights dominate at this size, but a 128K window still costs gigabytes.
Which quantization should you actually run?
Llama 3.3 70B Instruct is too large for a 12–16 GB card. On RTX A6000, Q4_K_M (42.6 GB) fits with room for everyday context lengths — the realistic single-GPU entry point. Higher quality (Q6_K 57.4 GB, FP16 138.8 GB) needs A100 80GB or multiple GPUs — for occasional use, renting is usually cheaper than buying.
Single-GPU fit at Q4_K_M (42.6 GB + KV)
RTX 3060 12GBRTX 4060 Ti 16GBRTX 4070 Ti SUPERRTX 3090RTX 4090RTX 5090A100 40GBRTX A6000L40SA100 80GBH100 80GBH200 141GB
Where the memory goes
Weights and overhead are exact for Q4_K_M. The KV bar is a generic 32-layer transformer at 8K; at this scale the weights dominate, but long context still adds gigabytes — size it in the calculator is for.
Deciding how much headroom to buy at this tier? RTX A6000 vs A100 80GB compares them on bandwidth, power and which models each one holds.
Common questions
How much VRAM does Llama 3.3 70B Instruct need?
Llama 3.3 70B Instruct needs about 42.6 GB of VRAM at Q4_K_M, 74.1 GB at Q8_0, or 138.8 GB at FP16. That is weights plus about 0.75 GB of runtime overhead and 5% of weight size; KV cache is additional and depends on context length.
What GPU can run Llama 3.3 70B Instruct?
At Q4_K_M the smallest card in our set that fits is the RTX A6000 (48 GB usable at 95%). At FP16 no single GPU in our set is sufficient.
Can Llama 3.3 70B Instruct run on 24 GB?
Not at Q4_K_M: it needs about 42.6 GB, more than the ~22.8 GB usable on a 24 GB card. Use a larger card, multiple GPUs, or offload.