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.
Quick Answer: RTX 3070 has 8GB VRAM, enough for models up to roughly 20B parameters at 4-bit quantization. It draws 220W under load.
With 8GB VRAM, RTX 3070 can run models up to approximately 20B parameters using 4-bit quantization. This is suitable for 7B-13B models like Llama 3 8B, Mistral 7B, and Qwen 7B.
Consider RTX 4070 or RTX 4080 — Significant performance increase for AI workloads.
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Essential accessories to pair with RTX 3070
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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
Showing 12 of 80 rows. Speeds are calculated estimates, not measurements — search for your model to jump straight to it.
| Model | Size | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|---|
| Deepseek AI Deepseek Coder 1.3B Instruct | 1.3B | Q4 | ~115 tok/sEstimated | 1GB |
| Deepseek AI Deepseek R1 Distill Qwen 1.5B | 1.5B | Q4 | ~115 tok/sEstimated | 1GB |
| Deepseek AI Deepseek Ocr 2 | Unknown | Q4 | ~95 tok/sEstimated | 2GB |
| Deepseek AI Deepseek Ocr | Unknown | Q4 | ~95 tok/sEstimated | 2GB |
| Lmstudio Community Deepseek R1 0528 Qwen3 8B Mlx 8bit | 8B | Q4 | ~95 tok/sEstimated | 4GB |
| Lmstudio Community Deepseek R1 0528 Qwen3 8B Mlx 4bit | 8B | Q4 | ~95 tok/sEstimated | 4GB |
| Deepseek AI Deepseek R1 Distill Qwen 7B | 7B | Q4 | ~95 tok/sEstimated | 4GB |
| Nineninesix Kani Tts 2 En | Unknown | Q4 | ~91 tok/sEstimated | 1GB |
| Qwen Qwen3 Tts 12hz 1 7B Customvoice | 7B | Q4 | ~91 tok/sEstimated | 1GB |
| Zai Org Glm Ocr | Unknown | Q4 | ~91 tok/sEstimated | 1GB |
| Qwen Qwen3 Asr 1 7B | 7B | Q4 | ~91 tok/sEstimated | 2GB |
| Nari Labs Dia2 2B | 2B | Q4 | ~91 tok/sEstimated | 1GB |
Showing 12 of 240 rows.
| Model | Size | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|---|
| 01 AI Yi 1 5 34B Chat | 34B | Q4 | Not supported | ~27 tok/sEstimated | 18GB (have 8GB) |
| 01 AI Yi 1 5 34B Chat | 34B | Q8 | Not supported | ~19 tok/sEstimated | 35GB (have 8GB) |
| 01 AI Yi 1 5 34B Chat | 34B | FP16 | Not supported | ~10 tok/sEstimated | 69GB (have 8GB) |
| AI Forever Rugpt 3.5 13B | 13B | Q4 | Fits (tight) | ~57 tok/sEstimated | 7GB (have 8GB) |
| AI Forever Rugpt 3.5 13B | 13B | Q8 | Not supported | ~40 tok/sEstimated | 13GB (have 8GB) |
| AI Forever Rugpt 3.5 13B | 13B | FP16 | Not supported | ~22 tok/sEstimated | 26GB (have 8GB) |
| AI Mo Kimina Prover 72B | 72B | Q4 | Not supported | ~15 tok/sEstimated | 37GB (have 8GB) |
| AI Mo Kimina Prover 72B | 72B | Q8 | Not supported | ~11 tok/sEstimated | 73GB (have 8GB) |
| AI Mo Kimina Prover 72B | 72B | FP16 | Not supported | ~5.8 tok/sEstimated | 146GB (have 8GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | Q4 | Fits comfortably | ~91 tok/sEstimated | 1GB (have 8GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | Q8 | Fits comfortably | ~64 tok/sEstimated | 2GB (have 8GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | FP16 | Fits comfortably | ~35 tok/sEstimated | 4GB (have 8GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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