Quick Answer: RTX 5090 offers 32GB VRAM and starts around $5196.32. It delivers approximately 395 tokens/sec on WeiboAI/VibeThinker-1.5B. It typically draws 575W under load.
This GPU offers reliable throughput for local AI workloads. Pair it with the right model quantization to hit your desired tokens/sec, and monitor prices below to catch the best deal.
Buy directly on Amazon with fast shipping and reliable customer service.
Rotate out primary variants whenever validation flags an issue.
💡 Not ready to buy? Try cloud GPUs first
Test RTX 5090 performance in the cloud before investing in hardware. Pay by the hour with no commitment.
| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| WeiboAI/VibeThinker-1.5B | Q4 | 395.40 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B | Q4 | 391.74 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 387.61 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 385.75 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-research/PowerMoE-3b | Q4 | 385.66 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 385.45 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 382.42 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 379.84 tok/sEstimated Auto-generated benchmark | 2GB |
| google-bert/bert-base-uncased | Q4 | 378.88 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 378.27 tok/sEstimated Auto-generated benchmark | 2GB |
| tencent/HunyuanOCR | Q4 | 376.53 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 367.39 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 363.30 tok/sEstimated Auto-generated benchmark | 1GB |
| google/embeddinggemma-300m | Q4 | 362.84 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 361.76 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 359.25 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 358.27 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 357.16 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 353.40 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 348.23 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 347.31 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-3-1b-it | Q4 | 342.38 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 338.05 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 337.52 tok/sEstimated Auto-generated benchmark | 2GB |
| facebook/sam3 | Q4 | 333.26 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 331.77 tok/sEstimated Auto-generated benchmark | 1GB |
| trl-internal-testing/tiny-random-LlamaForCausalLM | Q4 | 329.61 tok/sEstimated Auto-generated benchmark | 4GB |
| EleutherAI/pythia-70m-deduped | Q4 | 329.40 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.2-1B | Q4 | 329.17 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/Olmo-3-7B-Think | Q4 | 328.99 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-V3 | Q4 | 328.42 tok/sEstimated Auto-generated benchmark | 4GB |
| inference-net/Schematron-3B | Q4 | 328.13 tok/sEstimated Auto-generated benchmark | 2GB |
| openai-community/gpt2 | Q4 | 328.10 tok/sEstimated Auto-generated benchmark | 4GB |
| nari-labs/Dia2-2B | Q4 | 327.85 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-0.5B | Q4 | 327.73 tok/sEstimated Auto-generated benchmark | 3GB |
| microsoft/Phi-3.5-mini-instruct | Q4 | 326.64 tok/sEstimated Auto-generated benchmark | 4GB |
| google/gemma-2-2b-it | Q4 | 326.36 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen3-4B | Q4 | 326.12 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-1.5B | Q4 | 325.98 tok/sEstimated Auto-generated benchmark | 3GB |
| meta-llama/Llama-3.2-3B | Q4 | 325.57 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen3-Embedding-0.6B | Q4 | 325.05 tok/sEstimated Auto-generated benchmark | 3GB |
| microsoft/Phi-3.5-vision-instruct | Q4 | 323.72 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-Embedding-4B | Q4 | 323.49 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Meta-Llama-3-8B | Q4 | 323.23 tok/sEstimated Auto-generated benchmark | 4GB |
| petals-team/StableBeluga2 | Q4 | 323.08 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/VibeVoice-1.5B | Q4 | 322.58 tok/sEstimated Auto-generated benchmark | 3GB |
| microsoft/Phi-3.5-mini-instruct | Q4 | 322.16 tok/sEstimated Auto-generated benchmark | 2GB |
| Alibaba-NLP/gte-Qwen2-1.5B-instruct | Q4 | 322.15 tok/sEstimated Auto-generated benchmark | 3GB |
| unsloth/Meta-Llama-3.1-8B-Instruct | Q4 | 322.14 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-3.3-8b-instruct | Q4 | 319.72 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 |
|---|---|---|---|---|
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q4 | Fits comfortably | 314.53 tok/sEstimated | 4GB (have 32GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q8 | Fits comfortably | 199.31 tok/sEstimated | 7GB (have 32GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | FP16 | Fits comfortably | 108.02 tok/sEstimated | 15GB (have 32GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q4 | Fits comfortably | 276.78 tok/sEstimated | 2GB (have 32GB) |
| Qwen/Qwen3-4B-Instruct-2507 | FP16 | Fits comfortably | 115.36 tok/sEstimated | 9GB (have 32GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 331.77 tok/sEstimated | 1GB (have 32GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q8 | Fits comfortably | 262.31 tok/sEstimated | 1GB (have 32GB) |
| meta-llama/Llama-3.2-1B-Instruct | FP16 | Fits comfortably | 141.96 tok/sEstimated | 2GB (have 32GB) |
| Qwen/Qwen2.5-3B-Instruct | Q4 | Fits comfortably | 347.31 tok/sEstimated | 2GB (have 32GB) |
| meta-llama/Llama-3.2-3B-Instruct | Q8 | Fits comfortably | 262.24 tok/sEstimated | 3GB (have 32GB) |
| meta-llama/Llama-3.2-3B-Instruct | FP16 | Fits comfortably | 135.14 tok/sEstimated | 6GB (have 32GB) |
| vikhyatk/moondream2 | Q4 | Fits comfortably | 293.81 tok/sEstimated | 4GB (have 32GB) |
| Qwen/Qwen3-4B | FP16 | Fits comfortably | 114.67 tok/sEstimated | 9GB (have 32GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | Q4 | Fits comfortably | 170.80 tok/sEstimated | 15GB (have 32GB) |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | Q8 | Fits comfortably | 219.99 tok/sEstimated | 4GB (have 32GB) |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | FP16 | Fits comfortably | 104.76 tok/sEstimated | 9GB (have 32GB) |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | FP16 | Fits comfortably | 114.71 tok/sEstimated | 17GB (have 32GB) |
| meta-llama/Llama-3.3-70B-Instruct | Q4 | Not supported | 114.96 tok/sEstimated | 34GB (have 32GB) |
| Qwen/Qwen3-14B | FP16 | Fits comfortably | 83.71 tok/sEstimated | 29GB (have 32GB) |
| Qwen/Qwen2.5-0.5B | Q4 | Fits comfortably | 327.73 tok/sEstimated | 3GB (have 32GB) |
| Qwen/Qwen2.5-0.5B | Q8 | Fits comfortably | 219.03 tok/sEstimated | 5GB (have 32GB) |
| Qwen/Qwen2.5-0.5B | FP16 | Fits comfortably | 108.81 tok/sEstimated | 11GB (have 32GB) |
| meta-llama/Llama-3.1-70B-Instruct | Q4 | Not supported | 98.72 tok/sEstimated | 34GB (have 32GB) |
| meta-llama/Llama-3.1-70B-Instruct | Q8 | Not supported | 80.46 tok/sEstimated | 68GB (have 32GB) |
| meta-llama/Llama-3.1-70B-Instruct | FP16 | Not supported | 43.25 tok/sEstimated | 137GB (have 32GB) |
| microsoft/phi-2 | FP16 | Fits comfortably | 114.46 tok/sEstimated | 15GB (have 32GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q4 | Fits comfortably | 281.33 tok/sEstimated | 4GB (have 32GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q8 | Fits comfortably | 209.59 tok/sEstimated | 7GB (have 32GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | FP16 | Fits comfortably | 108.62 tok/sEstimated | 15GB (have 32GB) |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | FP16 | Fits comfortably | 112.22 tok/sEstimated | 17GB (have 32GB) |
| HuggingFaceTB/SmolLM2-135M | Q4 | Fits comfortably | 285.99 tok/sEstimated | 4GB (have 32GB) |
| HuggingFaceTB/SmolLM2-135M | Q8 | Fits comfortably | 222.22 tok/sEstimated | 7GB (have 32GB) |
| HuggingFaceTB/SmolLM2-135M | FP16 | Fits comfortably | 105.03 tok/sEstimated | 15GB (have 32GB) |
| zai-org/GLM-4.6-FP8 | Q4 | Fits comfortably | 293.47 tok/sEstimated | 4GB (have 32GB) |
| zai-org/GLM-4.6-FP8 | Q8 | Fits comfortably | 191.61 tok/sEstimated | 7GB (have 32GB) |
| microsoft/DialoGPT-medium | Q8 | Fits comfortably | 223.10 tok/sEstimated | 7GB (have 32GB) |
| microsoft/DialoGPT-medium | FP16 | Fits comfortably | 109.47 tok/sEstimated | 15GB (have 32GB) |
| Qwen/Qwen2-0.5B | Q4 | Fits comfortably | 318.24 tok/sEstimated | 3GB (have 32GB) |
| Qwen/Qwen2-0.5B | Q8 | Fits comfortably | 206.04 tok/sEstimated | 5GB (have 32GB) |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | Fits comfortably | 379.84 tok/sEstimated | 2GB (have 32GB) |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q8 | Fits comfortably | 260.06 tok/sEstimated | 3GB (have 32GB) |
| deepseek-ai/deepseek-coder-1.3b-instruct | FP16 | Fits comfortably | 138.21 tok/sEstimated | 6GB (have 32GB) |
| microsoft/phi-4 | Q4 | Fits comfortably | 299.03 tok/sEstimated | 4GB (have 32GB) |
| deepseek-ai/DeepSeek-V3.1 | Q8 | Fits comfortably | 190.75 tok/sEstimated | 7GB (have 32GB) |
| deepseek-ai/DeepSeek-V3.1 | FP16 | Fits comfortably | 118.91 tok/sEstimated | 15GB (have 32GB) |
| meta-llama/Llama-3.1-8B | Q4 | Fits comfortably | 314.94 tok/sEstimated | 4GB (have 32GB) |
| Qwen/Qwen2.5-32B-Instruct | Q4 | Fits comfortably | 96.11 tok/sEstimated | 16GB (have 32GB) |
| Qwen/Qwen2.5-32B-Instruct | Q8 | Not supported | 75.86 tok/sEstimated | 33GB (have 32GB) |
| Qwen/Qwen2.5-32B-Instruct | FP16 | Not supported | 37.47 tok/sEstimated | 66GB (have 32GB) |
| openai-community/gpt2 | Q8 | Fits comfortably | 208.40 tok/sEstimated | 7GB (have 32GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Explore how RTX 5070 stacks up for local inference workloads.
Explore how RTX 4060 Ti 16GB stacks up for local inference workloads.
Explore how RX 6800 XT stacks up for local inference workloads.
Explore how RTX 4070 Super stacks up for local inference workloads.
Explore how RTX 3080 stacks up for local inference workloads.