Quick Answer: RTX 4090 offers 24GB VRAM and starts around current market pricing. It delivers approximately 237 tokens/sec on meta-llama/Llama-3.2-1B. It typically draws 450W under load.
RTX 4090 remains the go-to GPU for local AI workloads. It runs every mainstream 70B model, sustains the fastest consumer inference speeds, and anchors premium builds that scale to production deployments.
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| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| meta-llama/Llama-3.2-1B | Q4 | 236.93 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 236.31 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 234.17 tok/sEstimated Auto-generated benchmark | 2GB |
| google-bert/bert-base-uncased | Q4 | 233.28 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 232.46 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 231.79 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 231.78 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 231.53 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 230.47 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 229.75 tok/sEstimated Auto-generated benchmark | 2GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 229.43 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 226.87 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 225.67 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 224.35 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2-2b-it | Q4 | 223.94 tok/sEstimated Auto-generated benchmark | 1GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 222.41 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 220.50 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 212.85 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 211.46 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 208.71 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 206.65 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2b | Q4 | 206.11 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 205.12 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 204.34 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 204.34 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 200.86 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 200.35 tok/sEstimated Auto-generated benchmark | 2GB |
| google-t5/t5-3b | Q4 | 200.19 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-research/PowerMoE-3b | Q4 | 199.86 tok/sEstimated Auto-generated benchmark | 2GB |
| Alibaba-NLP/gte-Qwen2-1.5B-instruct | Q4 | 197.47 tok/sEstimated Auto-generated benchmark | 3GB |
| MiniMaxAI/MiniMax-M2 | Q4 | 197.36 tok/sEstimated Auto-generated benchmark | 4GB |
| petals-team/StableBeluga2 | Q4 | 197.28 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B | Q4 | 197.19 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen3-Embedding-8B | Q4 | 197.13 tok/sEstimated Auto-generated benchmark | 4GB |
| allenai/OLMo-2-0425-1B | Q4 | 196.96 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen3-0.6B-Base | Q4 | 196.79 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen3-4B-Thinking-2507-FP8 | Q4 | 196.43 tok/sEstimated Auto-generated benchmark | 2GB |
| openai-community/gpt2 | Q4 | 196.28 tok/sEstimated Auto-generated benchmark | 4GB |
| dicta-il/dictalm2.0-instruct | Q4 | 196.11 tok/sEstimated Auto-generated benchmark | 4GB |
| openai-community/gpt2-xl | Q4 | 195.90 tok/sEstimated Auto-generated benchmark | 4GB |
| zai-org/GLM-4.5-Air | Q4 | 195.68 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-V3 | Q4 | 195.65 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-7B-Instruct | Q4 | 194.82 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 194.55 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-7B | Q4 | 193.74 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q4 | 193.51 tok/sEstimated Auto-generated benchmark | 4GB |
| swiss-ai/Apertus-8B-Instruct-2509 | Q4 | 193.22 tok/sEstimated Auto-generated benchmark | 4GB |
| black-forest-labs/FLUX.2-dev | Q4 | 193.09 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-8B | Q4 | 192.14 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-1.7B | Q4 | 191.89 tok/sEstimated Auto-generated benchmark | 4GB |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
| Model | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|
| microsoft/Phi-3.5-mini-instruct | Q4 | Fits comfortably | 178.16 tok/sEstimated | 2GB (have 24GB) |
| facebook/sam3 | Q8 | Fits comfortably | 151.19 tok/sEstimated | 1GB (have 24GB) |
| AI-MO/Kimina-Prover-72B | FP16 | Not supported | 14.46 tok/sEstimated | 141GB (have 24GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-4bit | Q4 | Fits comfortably | 89.81 tok/sEstimated | 15GB (have 24GB) |
| ai-forever/ruGPT-3.5-13B | Q4 | Fits comfortably | 132.50 tok/sEstimated | 7GB (have 24GB) |
| Qwen/Qwen2.5-72B-Instruct | Q4 | Not supported | 34.18 tok/sEstimated | 36GB (have 24GB) |
| ibm-research/PowerMoE-3b | Q8 | Fits comfortably | 144.77 tok/sEstimated | 3GB (have 24GB) |
| IlyaGusev/saiga_llama3_8b | Q4 | Fits comfortably | 169.42 tok/sEstimated | 4GB (have 24GB) |
| Qwen/Qwen2-7B-Instruct | FP16 | Fits comfortably | 62.57 tok/sEstimated | 15GB (have 24GB) |
| Qwen/Qwen3-4B-Base | Q4 | Fits comfortably | 178.74 tok/sEstimated | 2GB (have 24GB) |
| Qwen/Qwen3-4B-Base | Q8 | Fits comfortably | 123.29 tok/sEstimated | 4GB (have 24GB) |
| Qwen/Qwen2.5-14B | Q4 | Fits comfortably | 140.37 tok/sEstimated | 7GB (have 24GB) |
| Qwen/Qwen2.5-14B | Q8 | Fits comfortably | 93.37 tok/sEstimated | 14GB (have 24GB) |
| trl-internal-testing/tiny-LlamaForCausalLM-3.2 | Q4 | Fits comfortably | 184.39 tok/sEstimated | 4GB (have 24GB) |
| trl-internal-testing/tiny-LlamaForCausalLM-3.2 | Q8 | Fits comfortably | 121.22 tok/sEstimated | 7GB (have 24GB) |
| apple/OpenELM-1_1B-Instruct | FP16 | Fits comfortably | 82.25 tok/sEstimated | 2GB (have 24GB) |
| AI-MO/Kimina-Prover-72B | Q4 | Not supported | 34.32 tok/sEstimated | 35GB (have 24GB) |
| moonshotai/Kimi-K2-Thinking | Q8 | Not supported | 45.69 tok/sEstimated | 978GB (have 24GB) |
| moonshotai/Kimi-K2-Thinking | FP16 | Not supported | 23.80 tok/sEstimated | 1956GB (have 24GB) |
| deepseek-ai/DeepSeek-Math-V2 | Q8 | Not supported | 18.46 tok/sEstimated | 766GB (have 24GB) |
| deepseek-ai/DeepSeek-Math-V2 | FP16 | Not supported | 10.24 tok/sEstimated | 1532GB (have 24GB) |
| Tongyi-MAI/Z-Image-Turbo | Q4 | Fits comfortably | 178.19 tok/sEstimated | 4GB (have 24GB) |
| Tongyi-MAI/Z-Image-Turbo | Q8 | Fits comfortably | 127.07 tok/sEstimated | 8GB (have 24GB) |
| Tongyi-MAI/Z-Image-Turbo | FP16 | Fits comfortably | 74.91 tok/sEstimated | 16GB (have 24GB) |
| tencent/HunyuanOCR | Q8 | Fits comfortably | 164.42 tok/sEstimated | 2GB (have 24GB) |
| facebook/sam3 | FP16 | Fits comfortably | 88.62 tok/sEstimated | 2GB (have 24GB) |
| MiniMaxAI/MiniMax-VL-01 | Q4 | Not supported | 22.57 tok/sEstimated | 256GB (have 24GB) |
| MiniMaxAI/MiniMax-VL-01 | Q8 | Not supported | 13.66 tok/sEstimated | 511GB (have 24GB) |
| MiniMaxAI/MiniMax-VL-01 | FP16 | Not supported | 7.41 tok/sEstimated | 1021GB (have 24GB) |
| MiniMaxAI/MiniMax-M1-40k | Q4 | Not supported | 20.51 tok/sEstimated | 255GB (have 24GB) |
| MiniMaxAI/MiniMax-M1-40k | Q8 | Not supported | 16.20 tok/sEstimated | 510GB (have 24GB) |
| MiniMaxAI/MiniMax-M1-40k | FP16 | Not supported | 8.61 tok/sEstimated | 1020GB (have 24GB) |
| WeiboAI/VibeThinker-1.5B | Q4 | Fits comfortably | 229.43 tok/sEstimated | 1GB (have 24GB) |
| WeiboAI/VibeThinker-1.5B | Q8 | Fits comfortably | 162.28 tok/sEstimated | 2GB (have 24GB) |
| WeiboAI/VibeThinker-1.5B | FP16 | Fits comfortably | 87.59 tok/sEstimated | 4GB (have 24GB) |
| tencent/HunyuanVideo-1.5 | Q4 | Fits comfortably | 174.11 tok/sEstimated | 4GB (have 24GB) |
| tencent/HunyuanVideo-1.5 | Q8 | Fits comfortably | 132.51 tok/sEstimated | 8GB (have 24GB) |
| tencent/HunyuanVideo-1.5 | FP16 | Fits comfortably | 68.66 tok/sEstimated | 16GB (have 24GB) |
| nari-labs/Dia2-2B | Q4 | Fits comfortably | 225.67 tok/sEstimated | 2GB (have 24GB) |
| nari-labs/Dia2-2B | Q8 | Fits comfortably | 159.64 tok/sEstimated | 3GB (have 24GB) |
| nari-labs/Dia2-2B | FP16 | Fits comfortably | 84.00 tok/sEstimated | 5GB (have 24GB) |
| unsloth/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 208.71 tok/sEstimated | 1GB (have 24GB) |
| unsloth/Llama-3.2-1B-Instruct | Q8 | Fits comfortably | 145.22 tok/sEstimated | 1GB (have 24GB) |
| unsloth/Llama-3.2-1B-Instruct | FP16 | Fits comfortably | 86.07 tok/sEstimated | 2GB (have 24GB) |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-8bit | Q4 | Fits comfortably | 183.82 tok/sEstimated | 4GB (have 24GB) |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-8bit | Q8 | Fits comfortably | 133.08 tok/sEstimated | 9GB (have 24GB) |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-8bit | FP16 | Fits comfortably | 68.16 tok/sEstimated | 17GB (have 24GB) |
| Qwen/Qwen3-235B-A22B | Q4 | Not supported | 22.33 tok/sEstimated | 115GB (have 24GB) |
| ibm-granite/granite-docling-258M | FP16 | Fits comfortably | 74.85 tok/sEstimated | 15GB (have 24GB) |
| google/gemma-2-9b-it | FP16 | Fits comfortably | 47.99 tok/sEstimated | 20GB (have 24GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Data-backed answers pulled from community benchmarks, manufacturer specs, and live pricing.
Community llama.cpp benchmarks of the ubergarm/Qwen3-30B-A3B-GGUF build show the RTX 4090 sustaining roughly 150–160 tokens/sec with CUDA kernels, keeping decode latency under 7 ms per token.
Source: Reddit – /r/LocalLLaMA (mq59v1k)
No. Builders loading Llama 3.1 70B Q4_K_M report roughly half the tensor pages spilling to system RAM on a 24 GB 4090, which drags throughput because PCIe becomes the bottleneck. Multi-GPU setups or 48 GB cards avoid the spill.
Source: Reddit – /r/LocalLLaMA (mqcouez)
Power users running multi-4090 racks note that a single 4090 comfortably hosts one 32B-class model; parallel agents or MoE workloads need tensor parallelism across multiple GPUs to keep speeds high.
Source: Reddit – /r/LocalLLaMA (mqwkgv3)
NVIDIA rates the RTX 4090 at 450 W board power and recommends at least an 850 W PSU with the 16-pin 12VHPWR connector to maintain headroom for AI workloads.
Source: TechPowerUp – RTX 4090 Specs
Our price tracker (Nov 2025) shows Amazon at $1,599 in stock.
Source: Supabase price tracker snapshot – 2025-11-03
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