RTX 4080 balances throughput and efficiency. It crushes 8B–13B models, handles most 70B work with clever quantization, and stays manageable in terms of power and thermals.
Quick Answer: RTX 4080 has 16GB VRAM, enough for models up to roughly 40B parameters at 4-bit quantization. It draws 320W under load.
With 16GB VRAM, RTX 4080 can run models up to approximately 40B parameters using 4-bit quantization. That covers 13B-34B comfortably; 70B-class models only fit with heavy offloading, at much lower throughput.
Consider RTX 4090 — Double the VRAM for larger models.
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Data-backed answers pulled from community benchmarks, manufacturer specs, and live pricing.
Umbrella’s CUDA build runs the 16 GB chat preset for Llama 3.3 70B at roughly 10 tokens/sec on a stock RTX 4080—around 20× faster than older GGUF pipelines on the same card.
Source: Reddit – /r/LocalLLaMA (m7daipg)
Yes. One builder logged Llama 3.3 70B Q3_s at ~15 tok/s on Windows with Ollama, then jumped to ~30 tok/s after switching to Linux with ExLlama and performance-tuned CUDA kernels.
Source: Reddit – /r/LocalLLaMA (mi1gu0s)
RTX 4080 carries a 320 W board power rating, ships with 16 GB of GDDR6X, and uses the 16-pin 12VHPWR connector. NVIDIA recommends at least a 750 W PSU.
Source: TechPowerUp – RTX 4080 Specs
Only with heavy offloading. Users experimenting with DDR6 system memory and PCIe offload confirm that 70B models can run, but bandwidth limits keep throughput well below 24 GB cards.
Source: Reddit – /r/LocalLLaMA (m76rp0l)
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 | ~185 tok/sEstimated | 1GB |
| Deepseek AI Deepseek R1 Distill Qwen 1.5B | 1.5B | Q4 | ~185 tok/sEstimated | 1GB |
| Deepseek AI Deepseek Ocr 2 | Unknown | Q4 | ~155 tok/sEstimated | 2GB |
| Deepseek AI Deepseek Ocr | Unknown | Q4 | ~155 tok/sEstimated | 2GB |
| Lmstudio Community Deepseek R1 0528 Qwen3 8B Mlx 8bit | 8B | Q4 | ~155 tok/sEstimated | 4GB |
| Lmstudio Community Deepseek R1 0528 Qwen3 8B Mlx 4bit | 8B | Q4 | ~155 tok/sEstimated | 4GB |
| Deepseek AI Deepseek R1 Distill Qwen 7B | 7B | Q4 | ~155 tok/sEstimated | 4GB |
| Nineninesix Kani Tts 2 En | Unknown | Q4 | ~145 tok/sEstimated | 1GB |
| Qwen Qwen3 Tts 12hz 1 7B Customvoice | 7B | Q4 | ~145 tok/sEstimated | 1GB |
| Zai Org Glm Ocr | Unknown | Q4 | ~145 tok/sEstimated | 1GB |
| Qwen Qwen3 Asr 1 7B | 7B | Q4 | ~145 tok/sEstimated | 2GB |
| Nari Labs Dia2 2B | 2B | Q4 | ~145 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 | ~43 tok/sEstimated | 18GB (have 16GB) |
| 01 AI Yi 1 5 34B Chat | 34B | Q8 | Not supported | ~30 tok/sEstimated | 35GB (have 16GB) |
| 01 AI Yi 1 5 34B Chat | 34B | FP16 | Not supported | ~16 tok/sEstimated | 69GB (have 16GB) |
| AI Forever Rugpt 3.5 13B | 13B | Q4 | Fits comfortably | ~92 tok/sEstimated | 7GB (have 16GB) |
| AI Forever Rugpt 3.5 13B | 13B | Q8 | Fits comfortably | ~64 tok/sEstimated | 13GB (have 16GB) |
| AI Forever Rugpt 3.5 13B | 13B | FP16 | Not supported | ~35 tok/sEstimated | 26GB (have 16GB) |
| AI Mo Kimina Prover 72B | 72B | Q4 | Not supported | ~25 tok/sEstimated | 37GB (have 16GB) |
| AI Mo Kimina Prover 72B | 72B | Q8 | Not supported | ~17 tok/sEstimated | 73GB (have 16GB) |
| AI Mo Kimina Prover 72B | 72B | FP16 | Not supported | ~9.3 tok/sEstimated | 146GB (have 16GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | Q4 | Fits comfortably | ~145 tok/sEstimated | 1GB (have 16GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | Q8 | Fits comfortably | ~105 tok/sEstimated | 2GB (have 16GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | FP16 | Fits comfortably | ~56 tok/sEstimated | 4GB (have 16GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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