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 4070 has 12GB VRAM, enough for models up to roughly 30B parameters at 4-bit quantization. It draws 200W under load.
With 12GB VRAM, RTX 4070 can run models up to approximately 30B parameters using 4-bit quantization. This is suitable for 7B-13B models like Llama 3 8B, Mistral 7B, and Qwen 7B.
Consider RTX 4080 Super or RTX 4090 — More VRAM and cores for demanding workloads.
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Data-backed answers pulled from community benchmarks, manufacturer specs, and live pricing.
Bandwidth math from community benchmarking places the 12 GB RTX 4070 at about 10 tokens/sec on Llama 3 70B Q4 when all layers stay in VRAM and kernels hold ~70% efficiency.
Source: Reddit – /r/LocalLLaMA (meaafcw)
Misconfigured offload is common—a 4070 laptop user reported 1.7 tok/s until shifting more layers from system RAM onto the GPU in llama.cpp.
Source: Reddit – /r/LocalLLaMA (l2it43q)
Yes. Notebook owners upgrading from 32 GB to 64 GB system RAM note that larger context windows and higher quants stop thrashing once the extra memory is installed.
Source: Reddit – /r/LocalLLaMA (lrqupbc)
The RTX 4070 carries a 200 W board power, provides 12 GB GDDR6X, and uses the 16-pin 12VHPWR plug. NVIDIA advises pairing it with a 650 W PSU.
Source: TechPowerUp – RTX 4070 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 | ~125 tok/sEstimated | 1GB |
| Deepseek AI Deepseek R1 Distill Qwen 1.5B | 1.5B | Q4 | ~125 tok/sEstimated | 1GB |
| Deepseek AI Deepseek Ocr 2 | Unknown | Q4 | ~105 tok/sEstimated | 2GB |
| Deepseek AI Deepseek Ocr | Unknown | Q4 | ~105 tok/sEstimated | 2GB |
| Lmstudio Community Deepseek R1 0528 Qwen3 8B Mlx 8bit | 8B | Q4 | ~105 tok/sEstimated | 4GB |
| Lmstudio Community Deepseek R1 0528 Qwen3 8B Mlx 4bit | 8B | Q4 | ~105 tok/sEstimated | 4GB |
| Deepseek AI Deepseek R1 Distill Qwen 7B | 7B | Q4 | ~105 tok/sEstimated | 4GB |
| Nineninesix Kani Tts 2 En | Unknown | Q4 | ~100 tok/sEstimated | 1GB |
| Qwen Qwen3 Tts 12hz 1 7B Customvoice | 7B | Q4 | ~100 tok/sEstimated | 1GB |
| Zai Org Glm Ocr | Unknown | Q4 | ~100 tok/sEstimated | 1GB |
| Qwen Qwen3 Asr 1 7B | 7B | Q4 | ~100 tok/sEstimated | 2GB |
| Nari Labs Dia2 2B | 2B | Q4 | ~100 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 | ~29 tok/sEstimated | 18GB (have 12GB) |
| 01 AI Yi 1 5 34B Chat | 34B | Q8 | Not supported | ~21 tok/sEstimated | 35GB (have 12GB) |
| 01 AI Yi 1 5 34B Chat | 34B | FP16 | Not supported | ~11 tok/sEstimated | 69GB (have 12GB) |
| AI Forever Rugpt 3.5 13B | 13B | Q4 | Fits comfortably | ~63 tok/sEstimated | 7GB (have 12GB) |
| AI Forever Rugpt 3.5 13B | 13B | Q8 | Not supported | ~44 tok/sEstimated | 13GB (have 12GB) |
| AI Forever Rugpt 3.5 13B | 13B | FP16 | Not supported | ~24 tok/sEstimated | 26GB (have 12GB) |
| AI Mo Kimina Prover 72B | 72B | Q4 | Not supported | ~17 tok/sEstimated | 37GB (have 12GB) |
| AI Mo Kimina Prover 72B | 72B | Q8 | Not supported | ~12 tok/sEstimated | 73GB (have 12GB) |
| AI Mo Kimina Prover 72B | 72B | FP16 | Not supported | ~6.4 tok/sEstimated | 146GB (have 12GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | Q4 | Fits comfortably | ~100 tok/sEstimated | 1GB (have 12GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | Q8 | Fits comfortably | ~70 tok/sEstimated | 2GB (have 12GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | FP16 | Fits comfortably | ~38 tok/sEstimated | 4GB (have 12GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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