Can the GeForce RTX 3090 (used) run Llama 3.3 70B Instruct?
Only with CPU offload at Q4_K_M.
Llama 3.3 70B Instruct is 70.6B parameters. At Q4_K_M the weights are about 39.5 GiB; with the 1.5 GiB runtime reserve that is 41.0 GiB against the card's 24GB, about 17.0 GiB more than the card has.
All three quants
24GB card| Quant | Weights | With reserve | Verdict | Headroom |
|---|---|---|---|---|
| Q4_K_Mthe everyday quant | 39.5 GiB | 41.0 GiB | Only with CPU offload | short 17.0 GiB |
| Q8_0near-lossless | 69.7 GiB | 71.2 GiB | Does not fit | short 47.2 GiB |
| FP16full weights | 131.5 GiB | 133.0 GiB | Does not fit | short 109.0 GiB |
Verdict rules: fits comfortably when weights plus reserve sit within 85% of VRAM; tight when they fit with little left for context; CPU offload when they exceed VRAM by up to 2x (runs, slowly, with layers in system RAM); no beyond that. The KV cache per token depends on the model's architecture and is not modelled here.
Not this card. These fit it comfortably at Q4.
Nothing in the consumer catalog fits Llama 3.3 70B Instruct at Q4 on a single card; this is multi-GPU or datacenter territory, or a job for hosted inference (see PicksByModel for per-token pricing).
What the GeForce RTX 3090 (used) does run well → · Llama 3.3 70B Instruct on every card →
About the model
Llama 3.3 70B Instruct by Meta: 131,072-token native context, text, released 2024 under the Llama 3.3 Community License. Model card → · Hosted pricing on PicksByModel →