Can the GeForce RTX 4090 (used) run Mixtral 8x22B Instruct v0.1?
Does not fit at Q4_K_M.
Mixtral 8x22B Instruct v0.1 is 141B parameters (MoE; all experts resident). At Q4_K_M the weights are about 78.8 GiB; with the 1.5 GiB runtime reserve that is 80.3 GiB against the card's 24GB, about 56.3 GiB more than the card has.
All three quants
24GB card| Quant | Weights | With reserve | Verdict | Headroom |
|---|---|---|---|---|
| Q4_K_Mthe everyday quant | 78.8 GiB | 80.3 GiB | Does not fit | short 56.3 GiB |
| Q8_0near-lossless | 139.2 GiB | 140.7 GiB | Does not fit | short 116.7 GiB |
| FP16full weights | 262.6 GiB | 264.1 GiB | Does not fit | short 240.1 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 Mixtral 8x22B Instruct v0.1 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 4090 (used) does run well → · Mixtral 8x22B Instruct v0.1 on every card →
About the model
Mixtral 8x22B Instruct v0.1 by Mistral AI: 65,536-token native context, text, released 2024 under the Apache-2.0. Model card → · Hosted pricing on PicksByModel →