NVIDIA GeForce RTX 4070 Super
12 GB GDDR6X · 504 GB/s
From
$599
Estimated street price
VRAM
12 GB
Bandwidth
504 GB/s
TDP
220W
Models
26
Tier
Starter
The NVIDIA GeForce RTX 4070 Super with 12 GB GDDR6X VRAM can handle 26 AI models across chat, coding, ai_coding. Best performance: Llama 3.2 1B Instruct at 170 tok/s (excellent). For AI coding workflows, it supports the Starter AI Coding tier, good for 7–8B models. Current price: approximately $599.
Source: OwnRig methodology
12 GB
504 GB/s
GDDR6X
220W
3-slot, 244mm
Builder Capability: Starter AI Coding
Runs 7-8B models comfortably. Good for basic local code completion and small model experiments.
Inference Backends
The software stacks that matter most for real-world inference on this device.
CUDA
productionPrimary high-performance backend for NVIDIA inference workloads.
Vulkan
stableFallback backend for llama.cpp and related local runtimes.
What it can run
26 models| Arcee Trinity Mini 26B | Q3_K_M | 7 tok/s | Not viable |
| Arcee Trinity Nano 6B | Q8_0 | 89 tok/s | Excellent |
| DeepSeek V3 | Q2_K | – | Not viable |
| Gemma 3 27B | Q3_K_M | – | Not viable |
| Gemma 3 4B | Q8_0 | 85 tok/s | Excellent |
| Gemma 4 26B-A4B | Q3_K_M | 8 tok/s | Not viable |
| Gemma 4 E2B | Q8_0 | 76 tok/s | Excellent |
| Gemma 4 E4B | Q8_0 | 47 tok/s | Good |
| GigaChat Lightning 10B | Q4_K_M | 96 tok/s | Acceptable |
| Llama 3.1 8B Instruct | Q5_K_M | 55 tok/s | Excellent |
| Llama 3.2 11B Vision | Q6_K | 48 tok/s | Good |
| Llama 3.2 1B Instruct | Q8_0 | 170 tok/s | Excellent |
| Llama 3.2 3B Instruct | Q8_0 | 110 tok/s | Excellent |
| Mistral 7B Instruct v0.3 | Q5_K_M | 50 tok/s | Excellent |
| nomic-embed-text v1.5 | FP16 | – | Excellent |
| NVIDIA Nemotron-3-super-120B-A12B | Q2_K | – | Not viable |
| Phi-3 Mini 3.8B Instruct | Q8_0 | 95 tok/s | Excellent |
| Phi-4 Mini | Q8_0 | 100 tok/s | Excellent |
| Qwen 2.5 7B Instruct | Q5_K_M | 52 tok/s | Excellent |
| Qwen3-8B Instruct | Q8_0 | 32 tok/s | Good |
| Qwen3.5-122B-A10B | Q3_K_M | – | Not viable |
| Qwen3.5-27B | Q3_K_M | 9 tok/s | Marginal |
| Qwen3.5-397B (MoE) | Q2_K | – | Not viable |
| Qwen3.6-27B | Q3_K_M | – | Not viable |
| Stable Diffusion 3.5 Large | Q8_0 | – | Good |
| Whisper Large V3 Turbo | FP16 | – | Excellent |
Showing 26 of 26 entries
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Frequently Asked Questions
- What AI models can NVIDIA GeForce RTX 4070 Super run?
- The NVIDIA GeForce RTX 4070 Super can run 26 AI models. Top performers include Llama 3.2 1B Instruct, Llama 3.2 3B Instruct, Phi-4 Mini. See the full compatibility table above for speeds and quality ratings.
- Is NVIDIA GeForce RTX 4070 Super good for AI coding?
- With 12 GB, the NVIDIA GeForce RTX 4070 Super runs 7-8B coding models at the Starter tier. Good for basic code completion.
- How much VRAM does NVIDIA GeForce RTX 4070 Super have?
- The NVIDIA GeForce RTX 4070 Super has 12 GB of GDDR6X VRAM with 504 GB/s bandwidth.
- Can NVIDIA GeForce RTX 4070 Super run 70B models?
- 70B models can run on the NVIDIA GeForce RTX 4070 Super with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.
- Is NVIDIA GeForce RTX 4070 Super worth it for AI?
- At $599, the NVIDIA GeForce RTX 4070 Super offers 12 GB VRAM and runs 26 AI models. It works for smaller models and experimentation.
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See every AI model it supports, expected performance, and how to build around it.
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