Quick Answer: RTX 3070 offers 8GB VRAM and starts around $319.99. It delivers approximately 100 tokens/sec on Qwen/Qwen2.5-3B. It typically draws 220W 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.
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Rotate out primary variants whenever validation flags an issue.
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| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| Qwen/Qwen2.5-3B | Q4 | 100.35 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 99.16 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 99.08 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2b | Q4 | 98.10 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 97.26 tok/sEstimated Auto-generated benchmark | 2GB |
| facebook/sam3 | Q4 | 95.54 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 95.44 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/OLMo-2-0425-1B | Q4 | 94.75 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 94.60 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 94.59 tok/sEstimated Auto-generated benchmark | 2GB |
| google-t5/t5-3b | Q4 | 92.71 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B | Q4 | 92.60 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 92.25 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 91.54 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 91.30 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 90.68 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-research/PowerMoE-3b | Q4 | 89.86 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 89.36 tok/sEstimated Auto-generated benchmark | 1GB |
| google-bert/bert-base-uncased | Q4 | 89.18 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 89.03 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 88.85 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 88.81 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 88.64 tok/sEstimated Auto-generated benchmark | 2GB |
| tencent/HunyuanOCR | Q4 | 88.52 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 88.24 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 87.29 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/gemma-3-1b-it | Q4 | 85.43 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/opt-125m | Q4 | 83.61 tok/sEstimated Auto-generated benchmark | 4GB |
| mistralai/Mistral-7B-Instruct-v0.1 | Q4 | 83.61 tok/sEstimated Auto-generated benchmark | 4GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 83.53 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen3-Embedding-8B | Q4 | 83.52 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-V3 | Q4 | 83.34 tok/sEstimated Auto-generated benchmark | 4GB |
| lmsys/vicuna-7b-v1.5 | Q4 | 83.30 tok/sEstimated Auto-generated benchmark | 4GB |
| tencent/HunyuanVideo-1.5 | Q4 | 82.97 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-0.5B | Q4 | 82.78 tok/sEstimated Auto-generated benchmark | 3GB |
| rednote-hilab/dots.ocr | Q4 | 82.74 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-0.5B-Instruct | Q4 | 82.73 tok/sEstimated Auto-generated benchmark | 3GB |
| HuggingFaceM4/tiny-random-LlamaForCausalLM | Q4 | 82.71 tok/sEstimated Auto-generated benchmark | 4GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 82.59 tok/sEstimated Auto-generated benchmark | 1GB |
| LiquidAI/LFM2-1.2B | Q4 | 82.58 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.1-8B | Q4 | 82.52 tok/sEstimated Auto-generated benchmark | 4GB |
| black-forest-labs/FLUX.1-dev | Q4 | 82.47 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.2-3B | Q4 | 82.29 tok/sEstimated Auto-generated benchmark | 2GB |
| skt/kogpt2-base-v2 | Q4 | 81.81 tok/sEstimated Auto-generated benchmark | 4GB |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | Q4 | 81.72 tok/sEstimated Auto-generated benchmark | 4GB |
| HuggingFaceTB/SmolLM2-135M | Q4 | 81.61 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen-Image-Edit-2509 | Q4 | 81.55 tok/sEstimated Auto-generated benchmark | 4GB |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-4bit | Q4 | 81.52 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2-7B-Instruct | Q4 | 81.51 tok/sEstimated Auto-generated benchmark | 4GB |
| liuhaotian/llava-v1.5-7b | Q4 | 81.50 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 |
|---|---|---|---|---|
| Qwen/Qwen2-1.5B-Instruct | FP16 | Not supported | 29.16 tok/sEstimated | 11GB (have 8GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | FP16 | Not supported | 30.76 tok/sEstimated | 11GB (have 8GB) |
| microsoft/Phi-3-mini-4k-instruct | Q4 | Fits comfortably | 71.81 tok/sEstimated | 4GB (have 8GB) |
| microsoft/Phi-3-mini-4k-instruct | Q8 | Fits (tight) | 50.32 tok/sEstimated | 7GB (have 8GB) |
| microsoft/Phi-3-mini-4k-instruct | FP16 | Not supported | 28.33 tok/sEstimated | 15GB (have 8GB) |
| openai-community/gpt2-large | Q4 | Fits comfortably | 79.55 tok/sEstimated | 4GB (have 8GB) |
| openai-community/gpt2-large | Q8 | Fits (tight) | 52.59 tok/sEstimated | 7GB (have 8GB) |
| openai-community/gpt2-large | FP16 | Not supported | 31.47 tok/sEstimated | 15GB (have 8GB) |
| Qwen/Qwen3-1.7B | Q4 | Fits comfortably | 74.27 tok/sEstimated | 4GB (have 8GB) |
| Qwen/Qwen3-1.7B | Q8 | Fits (tight) | 52.30 tok/sEstimated | 7GB (have 8GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | Q4 | Not supported | 42.24 tok/sEstimated | 15GB (have 8GB) |
| google-t5/t5-3b | Q4 | Fits comfortably | 92.71 tok/sEstimated | 2GB (have 8GB) |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | Q8 | Fits comfortably | 54.04 tok/sEstimated | 4GB (have 8GB) |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | FP16 | Not supported | 26.07 tok/sEstimated | 9GB (have 8GB) |
| Qwen/Qwen3-30B-A3B | Q8 | Not supported | 29.22 tok/sEstimated | 31GB (have 8GB) |
| microsoft/Phi-3.5-mini-instruct | Q4 | Fits comfortably | 78.68 tok/sEstimated | 4GB (have 8GB) |
| unsloth/Llama-3.2-3B-Instruct | Q8 | Fits comfortably | 67.22 tok/sEstimated | 3GB (have 8GB) |
| unsloth/Llama-3.2-3B-Instruct | FP16 | Fits comfortably | 36.58 tok/sEstimated | 6GB (have 8GB) |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-8bit | FP16 | Not supported | 27.06 tok/sEstimated | 9GB (have 8GB) |
| ibm-research/PowerMoE-3b | Q4 | Fits comfortably | 89.86 tok/sEstimated | 2GB (have 8GB) |
| microsoft/Phi-3.5-mini-instruct | Q4 | Fits comfortably | 81.12 tok/sEstimated | 2GB (have 8GB) |
| microsoft/Phi-3.5-mini-instruct | Q8 | Fits comfortably | 57.32 tok/sEstimated | 4GB (have 8GB) |
| WeiboAI/VibeThinker-1.5B | Q4 | Fits comfortably | 82.59 tok/sEstimated | 1GB (have 8GB) |
| WeiboAI/VibeThinker-1.5B | Q8 | Fits comfortably | 59.54 tok/sEstimated | 2GB (have 8GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q8 | Fits comfortably | 54.45 tok/sEstimated | 5GB (have 8GB) |
| Qwen/Qwen2.5-1.5B-Instruct | FP16 | Not supported | 28.60 tok/sEstimated | 11GB (have 8GB) |
| facebook/opt-125m | Q4 | Fits comfortably | 83.61 tok/sEstimated | 4GB (have 8GB) |
| meta-llama/Meta-Llama-3-8B | Q8 | Not supported | 48.60 tok/sEstimated | 9GB (have 8GB) |
| meta-llama/Llama-3.3-70B-Instruct | Q4 | Not supported | 25.54 tok/sEstimated | 34GB (have 8GB) |
| meta-llama/Llama-3.3-70B-Instruct | Q8 | Not supported | 17.55 tok/sEstimated | 68GB (have 8GB) |
| deepseek-ai/DeepSeek-R1-0528 | Q4 | Fits comfortably | 69.44 tok/sEstimated | 4GB (have 8GB) |
| deepseek-ai/DeepSeek-R1-0528 | Q8 | Fits (tight) | 50.63 tok/sEstimated | 7GB (have 8GB) |
| deepseek-ai/DeepSeek-R1-0528 | FP16 | Not supported | 29.37 tok/sEstimated | 15GB (have 8GB) |
| Qwen/Qwen2.5-32B-Instruct | Q4 | Not supported | 29.32 tok/sEstimated | 16GB (have 8GB) |
| HuggingFaceTB/SmolLM-135M | FP16 | Not supported | 27.66 tok/sEstimated | 15GB (have 8GB) |
| Qwen/Qwen2.5-Math-1.5B | Q4 | Fits comfortably | 72.32 tok/sEstimated | 3GB (have 8GB) |
| Qwen/Qwen2.5-Math-1.5B | FP16 | Not supported | 28.54 tok/sEstimated | 11GB (have 8GB) |
| rinna/japanese-gpt-neox-small | Q8 | Fits (tight) | 55.72 tok/sEstimated | 7GB (have 8GB) |
| google/gemma-2-2b-it | Q8 | Fits comfortably | 58.05 tok/sEstimated | 2GB (have 8GB) |
| google/gemma-2-2b-it | FP16 | Fits comfortably | 32.22 tok/sEstimated | 4GB (have 8GB) |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-6bit | Q8 | Fits comfortably | 54.73 tok/sEstimated | 4GB (have 8GB) |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-6bit | FP16 | Not supported | 27.65 tok/sEstimated | 9GB (have 8GB) |
| RedHatAI/Meta-Llama-3.1-70B-Instruct-quantized.w4a16 | Q4 | Not supported | 25.94 tok/sEstimated | 34GB (have 8GB) |
| llamafactory/tiny-random-Llama-3 | FP16 | Not supported | 27.28 tok/sEstimated | 15GB (have 8GB) |
| liuhaotian/llava-v1.5-7b | Q4 | Fits comfortably | 81.50 tok/sEstimated | 4GB (have 8GB) |
| liuhaotian/llava-v1.5-7b | Q8 | Fits (tight) | 54.31 tok/sEstimated | 7GB (have 8GB) |
| liuhaotian/llava-v1.5-7b | FP16 | Not supported | 29.03 tok/sEstimated | 15GB (have 8GB) |
| Qwen/Qwen2.5-72B-Instruct | Q4 | Not supported | 14.33 tok/sEstimated | 35GB (have 8GB) |
| Qwen/Qwen3-4B-Thinking-2507-FP8 | Q8 | Fits comfortably | 51.93 tok/sEstimated | 4GB (have 8GB) |
| meta-llama/Meta-Llama-3-8B | Q4 | Fits comfortably | 80.09 tok/sEstimated | 4GB (have 8GB) |
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.
A Windows builder running LLaMA 2 13B Q6 in Kobold with system-memory fallback enabled sees about 8 tokens/sec, while disabling fallback drops throughput toward 5 tok/s.
Source: Reddit – /r/LocalLLaMA (1beu2vh)
Not really—community buyers warn that the 3070’s 8GB ceiling struggles beyond 13B unless you chain multiple cards or accept heavy offload, making higher-VRAM GPUs a safer bet.
Source: Reddit – /r/LocalLLaMA (ndp8799)
Keep NVIDIA’s sysmem fallback enabled when you stretch past 7B models—users disabling it see token speeds collapse as layers spill to host RAM instead of the GPU.
Source: Reddit – /r/LocalLLaMA (kuyjopm)
RTX 3070 ships with 8 GB GDDR6, draws 220 W, and uses dual 8-pin PCIe connectors with NVIDIA recommending a 650 W PSU.
Source: TechPowerUp – RTX 3070 Specs
As of Nov 2025 the RTX 3070 was around $499 on Amazon, in stock.
Source: Supabase price tracker snapshot – 2025-11-03
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