RTX 4090 24GB Anything that fits into 24GB is fast. The limit is also exactly 24GB.
This is the machine to look at if you want to quickly process LLM and image creation that fit in 24GB and avoid the new high price.
Speed and memory for the same model
Hardware core specifications
- Available memory
- 22.5GB
- Memory bandwidth
- 1008GB/s
- Estimated sustained LLM load
- About 470W
- Korean price range (KRW)
- 325 × KRW 10,000–400 × KRW 10,000
Who it fits
- Fast 12B to 27B inference
- ComfyUI image generation
- CUDA development environment
Skip it if
Models with more than 24GB cannot be loaded even if they are fast, and their appeal diminishes if used prices are close to the 5090.
Recommended format for each device 4K input
The model you will experience with this equipment
K2 Horizon 0.9B
Required 1.03GB · spare time
llama.cpp CUDA · Q4_K_M
Decode
1426.4 ~ 1578.6 tok/s
First token
0.16 to 0.22 seconds
Qwen3.8 27B
recommendedRequired 17.56GB · titration
llama.cpp CUDA · Q4_K_M
Decode
38.3 ~ 51.9 tok/s
First token
2.6 to 3.6 seconds
Qwen3.8-Flash-Next
Required 74.5561152GB · tight
Strata 0.1.33 serving branch · IQ3_S · MTP-4 · INT8 KV · SSD PLE
Decode
92.6 ~ 192.3 tok/s
First token
0.72 to 1.5 seconds
Actual speed varies with runtime version, cooling, context length and quantization file.
purchase judgment
What tasks is it suitable for?
27B conversation
comfortableHigh single GPU speed within 24GB.
Create image
comfortableCompare the processing speed of the same model together with our support tools.
large model
ConditionalIf you exceed VRAM, CPU/RAM offloading loss is significant.
- The first thing to get stuck
- The 24GB VRAM limit is reached before compute performance.
- Configuration to check
- RTX 4090 24GB · System RAM 64GB · 1000W power · 4 slots available
- Criteria for spending more money
- It's worth it when the price difference over the 5090 is big enough and 24GB is enough. If you need 32GB, look no further than the 5090.
On sales pages, look at memory before chips.
The model and speed assumptions above change if the product is not the 24GB configuration.
Frequently asked questions
- What LLM can be run on an RTX 4090 24GB?
- K2 Horizon 0.9B, Qwen3.8 27B, Qwen3.8-Flash-Next and others with a 4K input and the recommended format. Required memory varies by model and context length.
- What is the power consumption of a local LLM on an RTX 4090 with 24GB?
- Sustained load is about 470 W, with an actual range of 350–600 W.
- Are the displayed token speeds ground truth?
- This is a range aligned to collected benchmark measurements under the same conditions. Combinations without a matching measurement are estimated from the nearest result and vary with runtime, cooling, quantization file and context length.
Equipment to compare together
Used cost-effectiveness
RTX 3090 24GB
If you own an RTX 3090, first check whether the target model and context fit in 24GB. Newer hardware alone is not a reason to replace a card that completes your work. For a used purchase, include power, cooling, case compatibility and return terms in the cost.
32GB VRAM
RTX 5090 32GB
Compare this card if you repeatedly generate images or run 27B–35B inference and want shorter waits. But 32GB is still a limit. Buying on compute performance alone, without checking the model, input and image resolution, may leave you offloading again.
large memory
Studio M4 Max 128GB
Suits people who want to compare a wide range from 70B-class to high-precision models in a single finished machine.