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NVIDIA
Operating mode
Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
The RTX 4090 is NVIDIA's flagship consumer GPU built on the Ada Lovelace architecture. With 24 GB of GDDR6X VRAM and 16,384 CUDA cores, it is among the most capable consumer cards for local AI inference. It can run 13B parameter models at full precision and 70B+ models with quantization, delivering class-leading decode speeds thanks to its massive tensor core count and 1 TB/s memory bandwidth.
Official product page ↗Beyond LLMs
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|---|---|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Runs natively | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
Architecture
Ada Lovelace is NVIDIA's fourth-generation RTX architecture, manufactured on TSMC's custom 4N process. It introduces 4th-generation Tensor Cores with FP8 support, 3rd-generation ray tracing cores, and the Shader Execution Reordering (SER) engine for improved workload scheduling.
AI Relevance
FP8 Tensor Core operations provide a significant uplift for quantized LLM inference compared to Ampere's FP16-only Tensor Cores. DLSS 3 Frame Generation demonstrates the architecture's AI processing capabilities.
Ada Lovelace is NVIDIA's fourth-generation RTX architecture, manufactured on TSMC's custom 4N process. It introduces 3rd-generation ray tracing cores, 4th-generation Tensor Cores with FP8 support, and the Shader Execution Reordering (SER) engine for improved workload scheduling.
The RTX 4090 features the full AD102 GPU die with 128 Streaming Multiprocessors (SMs), each containing 128 CUDA cores for a total of 16,384. Its 512 Tensor Cores can perform FP8 matrix operations at up to 1,321 TOPS, making it exceptionally efficient for quantized LLM inference.
The memory subsystem uses a 384-bit bus connected to 24 GB of Micron GDDR6X running at 21 Gbps, delivering 1,008 GB/s of bandwidth. For AI inference, this bandwidth is the primary bottleneck — it directly determines how many tokens per second the GPU can generate during autoregressive decoding.
Cost vs cloud API
Assumes 4 hours/day of active inference at 83 tok/s, RTX 4090 24GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
36.0M
Tokens/month at this pace
$50.7
Monthly local cost
$360
Same tokens on cloud API
$1.41
Local $/1M tokens
Break-even: pays for itself in 4.5 months vs cloud API at this workload. Price reference: $1.6k MSRP.
Chat
SQwen 3 14B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Coding
SCodestral 2 25.08 is a specialized fit for Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Agentic Coding
SJust out of reach
High-quality models that need a bit more memory
Image & Video Generation
41 of 52 models can generate images or video on your RTX 4090 24GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 500ms | S |
| Stable Diffusion 1.5Image | 512×768 | ~1s | S |
| Realistic Vision v5.1Image | 512×768 | ~1s | S |
| DreamShaper 8Image | 512×768 | ~1s | S |
| LCM DreamShaper v7 |
Multi-GPU scaling
Scale out with multiple GPUs for larger models. PCIe interconnect with 30% scaling overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|---|---|---|
| 1× RTX | 24 GB | 319/374 | 1,008 GB/s |
| 2× RTX | 48 GB | 338/374 | 1,411 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.7× per additional GPU.
Upgrade paths
See what you unlock with more powerful hardware
Upgrade options
Unlocks 19 additional models that do not fit on the current setup.
Scale-out only pays off if the host platform has enough PCIe lanes, slot spacing, power, and cooling.
The bigger the setup gets, the more the runtime matters. Multi-GPU and multi-user serving are where vLLM, SGLang, TGI, TensorRT-LLM, or tuned llama.cpp start to earn their complexity.
~$1,599 MSRP
Unlocks 1 additional models that do not fit on the current setup.
~$2,499 MSRP
Unlocks 6 additional models that do not fit on the current setup.
~$4,000 MSRP
Unlocks 17 additional models that do not fit on the current setup.
~$1,099 MSRP
Unlocks 45 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 97%.
~$8,000 MSRP
Compare with similar
Related guides
| Won’t fit |
| Llama 3.1 70B Q4 |
| — |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~4s per image |
| Image Gen (Flux) | Runs with offload | Flux.1 Dev FP16 | ~~18s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~22s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~3.5s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~10.2s/frame |
Qwen 3.6 27B is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It should run, but memory headroom will be limited. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Reasoning
SQwen 3 14B matches Reasoning and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
RAG
AGranite 4.1 8B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
| 512×768 |
| 300ms |
| S |
| PixArt-SigmaImage | 1024×1024 | ~4s | S |
| FramePack I2VVideo | 256×256 | ~7.3s/frame | S |
| SDXL TurboImage | 512×512 | 500ms | S |
| SDXL LightningImage | 1024×1024 | ~1.5s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~4s | S |
| Playground v2.5Image | 1024×1024 | ~6s | S |
| RealVisXL v5.0Image | 1024×1024 | ~4.5s | S |
| DreamShaper XLImage | 1024×1024 | ~4.5s | S |
| Juggernaut XL v9Image | 1024×1024 | ~4.5s | S |
| Animagine XL 3.1Image | 1024×1024 | ~4.5s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~4.5s | S |
| Animagine XL 4.0Image | 1024×1024 | ~4.5s | S |
| Illustrious XLImage | 1024×1024 | ~4.5s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~2.9s/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~7s | S |
| Flux.2 Klein 4BImage | 1024×1024 | ~1.2s | S |
| LTX Video 2BVideo | 768×512 | ~3.5s/frame | S |
| KolorsImage | 1024×1024 | ~8s | S |
| Stable CascadeImage | 1024×1024 | ~10s | S |
| AuraFlow v0.3Image | 1536×1536 | ~18s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~22s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | ~4s | S |
| CogVideoX 2BVideo | 720×480 | ~3.5s/frame | A |
| HunyuanVideoVideo | 256×256 | ~7.3s/frame | A |
| ChromaImage | 256×256 | ~7.3s | A |
| Z-Image TurboImage | 1536×1536 | ~4.1s | B |
| Flux.1 DevImage | 256×256 | ~18s | B |
| Flux.1 SchnellImage | 256×256 | ~3.5s | B |
| LTX Video 13BVideo | 256×256 | ~7.3s/frame | B |
| Flux.1 Kontext DevImage | 256×256 | ~20s | B |
| AnimateDiff v1.5.3Video | 512×768 | ~1.8s/frame | B |
| Cosmos Diffusion 7BVideo | 256×256 | ~11.1s/frame | B |
| CogVideoX 5BVideo | 256×256 | ~10.5s/frame | B |
| Wan2.2 TI2V 5BVideo | 256×256 | ~10.5s/frame | B |
| Flux.2 Klein 9BImage | 256×256 | ~3.7s | D |
| Flux.1 Fill DevImage | 256×256 | ~17s | D |
| Mochi 1 PreviewVideo | 256×256 | ~6.6s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~6.1s/frame | F |
| Helios 14BVideo | 256×256 | ~7.6s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~7.6s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~7.6s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~7.6s/frame | F |
| Qwen ImageImage | 256×256 | ~6.7s | F |
| Qwen Image EditImage | 256×256 | ~6.7s | F |
| Flux.2 DevImage | 256×256 | ~3m 9s | F |
| MAGI-1Video | 256×256 | ~9.4s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~11.9s | F |
Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
Buying advice
Excellent choice for local AI
Runs 26 of 50 top models well — a strong all-rounder for local inference.
24.0 GB
VRAM
$1,599
MSRP
$67/GB
Cost per GB VRAM
Best models for this GPU
What will limit you first
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best upgrade itinerary
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Unlocks 1 additional models that do not fit on the current setup.
Want more headroom? MacBook Pro M4 Max 36GB (36.0 GB unified memory) is the next step up.
