for Local LLM Inference
TL;DR
The RTX 4090 dominates the RTX 3090 for local LLM inference and training thanks to massive architecture gains, faster memory bandwidth, and new transformer engine support. However, the 3090 remains a viable budget option for users willing to trade speed for cost, especially if they already own one.
NVIDIA RTX 4090 wins for Local LLM Inference. The RTX 4090 dominates the RTX 3090 for local LLM inference and training thanks to massive architecture gains, faster memory bandwidth, and new transformer engine support. However, the 3090 remains a viable budget option for users willing to trade speed for cost, especially if they already own one.
Winner: GPU Architecture
NVIDIA RTX 4090
Winner: CUDA Cores
NVIDIA RTX 4090
Winner: Tensor Cores (Gen)
NVIDIA RTX 4090
Overall Pick
NVIDIA RTX 4090
| Specification | NVIDIA RTX 4090 | NVIDIA RTX 3090 |
|---|---|---|
| GPU Architecture | Ada Lovelace (TSMC 4N) | Ampere (Samsung 8nm) |
| CUDA Cores | 16384 | 10496 |
| Tensor Cores (Gen) | 4th Gen (512) | 3rd Gen (336) |
| RT Cores (Gen) | 3rd Gen (128) | 2nd Gen (82) |
| Base Clock | 2.23 GHz | 1.40 GHz |
| Boost Clock | 2.52 GHz | 1.70 GHz |
| Memory Size | 24 GB GDDR6X | 24 GB GDDR6X |
| Memory Bus Width | 384-bit | 384-bit |
| Memory Bandwidth | 1008 GB/s | 936 GB/s |
| L2 Cache | 72 MB | 6 MB |
| Transistor Count | 76.3B | 28.3B |
| FP16 (TFLOPS) | 165.2 (tensor) | 71.3 (tensor) |
| FP8 (TFLOPS) | 330.2 (tensor) | Not supported natively |
| TDP | 450W | 350W |
| NVLink Support | No | Yes (NVLink 3.0, 112.5 GB/s) |
Cross-referenced from our benchmark database , higher is better. Numbers are tokens/sec for LLM workloads, images/min for image workloads.
If you mostly
Run 13B-70B parameter models daily and need the fastest tokens/second, especially with 4-bit quantization.
Recommend
NVIDIA RTX 4090
The RTX 4090's massive L2 cache and higher memory bandwidth yield 2-3x faster token generation on models like Llama 2 70B (4-bit), making interactive chat and large context windows much smoother. The 3090 lags noticeably due to memory latency and lack of FP8 support.
If you mostly
Only run smaller 7B-13B models, have an existing 3090, or want to combine two for >24 GB VRAM via NVLink.
Recommend
NVIDIA RTX 3090
For small models, the 3090 is still fast enough, and its lower TDP and used price (often under $800) make it a more cost-effective choice. NVLink enables pooling 48GB for finetuning larger contexts, a capability the 4090 completely lacks.
If you mostly
Prioritize power efficiency, lower heat output, and run inference 24/7 on a single GPU for a home server.
Recommend
NVIDIA RTX 4090
Despite higher peak TDP, the 4090 completes tasks so much faster that its total energy per inference is often lower than the 3090. For sustained load, its architectural efficiency translates to less heat and noise in confined spaces.
The RTX 4090 costs roughly 2x the current used price of an RTX 3090 ($1600 vs $750) but delivers up to 2.5x the inference throughput, making it superior in raw performance per dollar for heavy workloads. However, the 3090 offers nearly double the VRAM scaling potential via NVLink, better performance per watt under sustained load for small models, and lower total cost of ownership if you already own it. For most local AI builders, the premium is worth it only if they regularly run 30B+ models or require FP8 acceleration.
NVIDIA RTX 4090
NVIDIA RTX 3090
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The RTX 4090 is the undisputed king for local LLM inference, especially with large 30B-70B parameter models. Its architectural leaps, 12x larger L2 cache, faster memory, and native FP8 tensor cores, translate to real-world token generation speeds 2-3x faster than the 3090, making it feel like a generational upgrade. If you plan to build a serious home AI rig for the next 2-3 years and have the budget, the 4090 is the easy choice; the 3090 simply can't match its compute efficiency or transformer engine optimizations.
However, the RTX 3090 remains a compelling option for budget-conscious builders or those who need more than 24GB of VRAM. With NVLink, two 3090s still cost less than one 4090 and offer 48GB pooled memory, critical for fine-tuning larger models or running long-context inference. The 3090 also draws less power under sustained load for smaller models, and its raw compute is still excellent for 7B-13B parameter quantized models. Don't buy a 4090 expecting it to scale with multiple GPUs; for multi-GPU setups, the 3090's NVLink advantage can outweigh its single-card performance deficit.
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