Quick Answer: RTX 4080 Super offers 16GB VRAM and starts around $1149.99. It delivers approximately 167 tokens/sec on deepseek-ai/DeepSeek-OCR. It typically draws 320W 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.
With 16GB VRAM, RTX 4080 Super can run models up to approximately 40B parameters using 4-bit quantization. This handles most popular models including Llama 3 70B, Mistral 7B, and larger.
Consider RTX 4090 — Double the VRAM for larger models.
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| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| deepseek-ai/DeepSeek-OCR | Q4 | 167.06 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 165.39 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 165.16 tok/sEstimated Auto-generated benchmark | 2GB |
| nari-labs/Dia2-2B | Q4 | 164.75 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 164.45 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen3-ASR-1.7B | Q4 | 160.65 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/OLMo-2-0425-1B | Q4 | 160.19 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-research/PowerMoE-3b | Q4 | 159.61 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-OCR-2 | Q4 | 157.92 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 157.86 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 157.82 tok/sEstimated Auto-generated benchmark | 2GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 157.22 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 156.66 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 154.73 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 152.34 tok/sEstimated Auto-generated benchmark | 2GB |
| google-bert/bert-base-uncased | Q4 | 152.27 tok/sEstimated Auto-generated benchmark | 1GB |
| LiquidAI/LFM2-1.2B | Q4 | 151.79 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 151.43 tok/sEstimated Auto-generated benchmark | 2GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 149.90 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 147.82 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 147.35 tok/sEstimated Auto-generated benchmark | 2GB |
| nineninesix/kani-tts-2-en | Q4 | 146.02 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2-2b-it | Q4 | 146.02 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 145.89 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-3-1b-it | Q4 | 144.27 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 144.05 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 143.94 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B | Q4 | 143.75 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 143.36 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 141.86 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 141.26 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 140.31 tok/sEstimated Auto-generated benchmark | 2GB |
| NousResearch/Meta-Llama-3.1-8B-Instruct | Q4 | 139.32 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-Embedding-4B | Q4 | 139.27 tok/sEstimated Auto-generated benchmark | 2GB |
| HuggingFaceTB/SmolLM2-135M | Q4 | 139.14 tok/sEstimated Auto-generated benchmark | 4GB |
| bigscience/bloomz-560m | Q4 | 138.95 tok/sEstimated Auto-generated benchmark | 4GB |
| rinna/japanese-gpt-neox-small | Q4 | 138.75 tok/sEstimated Auto-generated benchmark | 4GB |
| Nanbeige/Nanbeige4.1-3B | Q4 | 138.18 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen3-0.6B | Q4 | 138.12 tok/sEstimated Auto-generated benchmark | 3GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 138.02 tok/sEstimated Auto-generated benchmark | 1GB |
| EleutherAI/pythia-70m-deduped | Q4 | 137.86 tok/sEstimated Auto-generated benchmark | 4GB |
| zai-org/GLM-4.5-Air | Q4 | 137.86 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-0.5B | Q4 | 137.66 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen2.5-0.5B | Q4 | 137.20 tok/sEstimated Auto-generated benchmark | 3GB |
| facebook/sam3 | Q4 | 137.08 tok/sEstimated Auto-generated benchmark | 1GB |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-8bit | Q4 | 137.03 tok/sEstimated Auto-generated benchmark | 2GB |
| Alibaba-NLP/gte-Qwen2-1.5B-instruct | Q4 | 136.52 tok/sEstimated Auto-generated benchmark | 3GB |
| swiss-ai/Apertus-8B-Instruct-2509 | Q4 | 136.47 tok/sEstimated Auto-generated benchmark | 4GB |
| petals-team/StableBeluga2 | Q4 | 136.30 tok/sEstimated Auto-generated benchmark | 4GB |
| mistralai/Mistral-7B-v0.1 | Q4 | 136.12 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 |
|---|---|---|---|---|
| openai-community/gpt2 | Q8 | Fits comfortably | 90.94 tok/sEstimated | 7GB (have 16GB) |
| openai-community/gpt2 | FP16 | Fits (tight) | 43.40 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen2.5-7B-Instruct | Q4 | Fits comfortably | 124.92 tok/sEstimated | 4GB (have 16GB) |
| Qwen/Qwen2.5-7B-Instruct | Q8 | Fits comfortably | 80.04 tok/sEstimated | 7GB (have 16GB) |
| Qwen/Qwen2.5-7B-Instruct | FP16 | Fits (tight) | 51.59 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen3-0.6B | Q4 | Fits comfortably | 138.12 tok/sEstimated | 3GB (have 16GB) |
| Qwen/Qwen3-0.6B | Q8 | Fits comfortably | 85.34 tok/sEstimated | 6GB (have 16GB) |
| Qwen/Qwen3-0.6B | FP16 | Fits comfortably | 48.84 tok/sEstimated | 13GB (have 16GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | Q4 | Fits comfortably | 120.66 tok/sEstimated | 3GB (have 16GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | Q8 | Fits comfortably | 85.34 tok/sEstimated | 5GB (have 16GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | FP16 | Fits comfortably | 50.12 tok/sEstimated | 11GB (have 16GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q4 | Fits comfortably | 131.16 tok/sEstimated | 4GB (have 16GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q8 | Fits comfortably | 83.51 tok/sEstimated | 9GB (have 16GB) |
| meta-llama/Llama-3.1-8B-Instruct | FP16 | Not supported | 50.22 tok/sEstimated | 17GB (have 16GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q4 | Not supported | 47.41 tok/sEstimated | 17GB (have 16GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q8 | Not supported | 31.45 tok/sEstimated | 35GB (have 16GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | FP16 | Not supported | 15.77 tok/sEstimated | 70GB (have 16GB) |
| openai/gpt-oss-20b | Q4 | Fits comfortably | 65.14 tok/sEstimated | 10GB (have 16GB) |
| openai/gpt-oss-20b | Q8 | Not supported | 50.01 tok/sEstimated | 20GB (have 16GB) |
| openai/gpt-oss-20b | FP16 | Not supported | 26.55 tok/sEstimated | 41GB (have 16GB) |
| google/gemma-3-1b-it | Q4 | Fits comfortably | 144.27 tok/sEstimated | 1GB (have 16GB) |
| google/gemma-3-1b-it | Q8 | Fits comfortably | 114.67 tok/sEstimated | 1GB (have 16GB) |
| google/gemma-3-1b-it | FP16 | Fits comfortably | 56.61 tok/sEstimated | 2GB (have 16GB) |
| Qwen/Qwen3-Embedding-0.6B | Q4 | Fits comfortably | 114.07 tok/sEstimated | 3GB (have 16GB) |
| Qwen/Qwen3-Embedding-0.6B | Q8 | Fits comfortably | 93.40 tok/sEstimated | 6GB (have 16GB) |
| Qwen/Qwen3-Embedding-0.6B | FP16 | Fits comfortably | 52.35 tok/sEstimated | 13GB (have 16GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q4 | Fits comfortably | 125.98 tok/sEstimated | 3GB (have 16GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q8 | Fits comfortably | 81.29 tok/sEstimated | 5GB (have 16GB) |
| Qwen/Qwen2.5-1.5B-Instruct | FP16 | Fits comfortably | 46.50 tok/sEstimated | 11GB (have 16GB) |
| facebook/opt-125m | Q4 | Fits comfortably | 134.33 tok/sEstimated | 4GB (have 16GB) |
| facebook/opt-125m | Q8 | Fits comfortably | 92.73 tok/sEstimated | 7GB (have 16GB) |
| facebook/opt-125m | FP16 | Fits (tight) | 48.64 tok/sEstimated | 15GB (have 16GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | Fits comfortably | 149.90 tok/sEstimated | 1GB (have 16GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q8 | Fits comfortably | 106.88 tok/sEstimated | 1GB (have 16GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | FP16 | Fits comfortably | 56.07 tok/sEstimated | 2GB (have 16GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q4 | Fits comfortably | 115.10 tok/sEstimated | 4GB (have 16GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q8 | Fits comfortably | 91.69 tok/sEstimated | 7GB (have 16GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | FP16 | Fits (tight) | 52.40 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q4 | Fits comfortably | 115.41 tok/sEstimated | 2GB (have 16GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q8 | Fits comfortably | 94.79 tok/sEstimated | 4GB (have 16GB) |
| Qwen/Qwen3-4B-Instruct-2507 | FP16 | Fits comfortably | 45.22 tok/sEstimated | 9GB (have 16GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 157.86 tok/sEstimated | 1GB (have 16GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q8 | Fits comfortably | 104.72 tok/sEstimated | 1GB (have 16GB) |
| meta-llama/Llama-3.2-1B-Instruct | FP16 | Fits comfortably | 56.70 tok/sEstimated | 2GB (have 16GB) |
| openai/gpt-oss-120b | Q4 | Not supported | 25.69 tok/sEstimated | 59GB (have 16GB) |
| openai/gpt-oss-120b | Q8 | Not supported | 17.04 tok/sEstimated | 117GB (have 16GB) |
| bigscience/bloomz-560m | FP16 | Fits (tight) | 51.18 tok/sEstimated | 15GB (have 16GB) |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | Fits comfortably | 144.05 tok/sEstimated | 2GB (have 16GB) |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q8 | Fits comfortably | 111.00 tok/sEstimated | 3GB (have 16GB) |
| openai-community/gpt2 | Q4 | Fits comfortably | 122.86 tok/sEstimated | 4GB (have 16GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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