GeForce RTX 4070 Ti SUPER
16 GB GDDR6X · 672 GB/s · 285 W
RTX 4070 Ti SUPER 16 GB · 64 GB RAM → gpt-oss-20b
GPU · NVIDIA
RTX 4070 Ti SUPER for local LLMs: which models run?
With 16 GB of VRAM and 672 GB/s of bandwidth, the RTX 4070 Ti SUPER runs models up to about gpt-oss-20b entirely on the GPU.
- VRAM16 GB GDDR6X
- Bandwidth672 GB/s
- Board power285 W
- ArchitectureAda Lovelace
Specifications
- VRAM
- 16 GB
- Memory type
- GDDR6X
- Memory bus
- 256-bit
- Bandwidth
- 672 GB/s
- Board power
- 285 W
- Backends
- CUDA, Vulkan
- Interface
- PCIe 4.0 x16
- Released
- January 2024
- Segment
- Consumer
- FP16 compute
- Not available
Official specData updated 24 days agoSource: NVIDIA specifications
What the RTX 4070 Ti SUPER runs
| gpt-oss-20bMXFP4 · 8K~13 GB · 85–234 tok/s estimate | Excellent |
|---|---|
| Gemma 2 9B ITQ8_0 · 8K~12.7 GB · 43–58 tok/s estimate | Excellent |
| Qwen3 8BQ8_0 · 8K~10.5 GB · 50–68 tok/s estimate | Excellent |
| Llama 3.1 8B InstructQ8_0 · 8K~10.2 GB · 52–70 tok/s estimate | Excellent |
| DeepSeek R1 Distill Llama 8BQ8_0 · 8K~10.2 GB · 52–70 tok/s estimate | Excellent |
| Qwen2.5 7B InstructQ8_0 · 8K~9.2 GB · 56–76 tok/s estimate | Excellent |
| Mistral 7B Instruct v0.3Q8_0 · 8K~9.2 GB · 56–77 tok/s estimate | Excellent |
| Gemma 3 4B ITQ8_0 · 8K~5.4 GB · 97–140 tok/s estimate | Excellent |
| Qwen3 4BQ8_0 · 8K~6.2 GB · 86–124 tok/s estimate | Excellent |
| Phi-4 Mini Instruct 3.8BQ8_0 · 8K~6 GB · 92–132 tok/s estimate | Excellent |
| Llama 3.2 3B InstructQ8_0 · 8K~5.1 GB · 109–156 tok/s estimate | Excellent |
| Qwen3 1.7BQ8_0 · 8K~3.7 GB · 164–248 tok/s estimate | Excellent |
| Llama 3.2 1B InstructQ8_0 · 8K~2.5 GB · 266–398 tok/s estimate | Excellent |
| Qwen3 0.6BQ8_0 · 8K~2.5 GB · 262–448 tok/s estimate | Excellent |
| Mistral Small 3.2 24B InstructIQ4_XS · 8K~14.6 GB · 35–48 tok/s estimate | Compatible |
| Qwen3 14BQ6_K · 8K~14 GB · 37–50 tok/s estimate | Compatible |
| DeepSeek R1 Distill Qwen 14BQ6_K · 8K~14.3 GB · 36–49 tok/s estimate | Compatible |
| Qwen2.5 14B InstructQ6_K · 8K~14.3 GB · 36–49 tok/s estimate | Compatible |
| Phi-4 14BQ6_K · 8K~14.2 GB · 37–50 tok/s estimate | Compatible |
| Mistral Nemo 12B InstructQ8_0 · 8K~14.9 GB · 35–47 tok/s estimate | Compatible |
| Gemma 3 12B ITQ8_0 · 8K~14.1 GB · 36–49 tok/s estimate | Compatible |
| Qwen3 30B-A3BQ3_K_M · 8K~15.4 GB · 114–187 tok/s estimate | Compatible with limits |
| Gemma 3 27B ITQ3_K_M · 8K~15.3 GB · 33–45 tok/s estimate | Compatible with limits |
| Mixtral 8x7B InstructQ3_K_M · 8K~21.6 GB · 9.5–19 tok/s estimate | CPU/GPU offload |
| Qwen3 32BQ3_K_M · 8K~18.6 GB · 5.5–11 tok/s estimate | CPU/GPU offload |
| DeepSeek R1 Distill Qwen 32BQ3_K_M · 8K~18.5 GB · 5.5–11 tok/s estimate | CPU/GPU offload |
| Qwen2.5 32B InstructQ3_K_M · 8K~18.5 GB · 5.5–11 tok/s estimate | CPU/GPU offload |
| Qwen2.5 Coder 32B InstructQ3_K_M · 8K~18.5 GB · 5.5–11 tok/s estimate | CPU/GPU offload |
| QwQ 32BQ3_K_M · 8K~18.5 GB · 5.5–11 tok/s estimate | CPU/GPU offload |
| Qwen3 Coder 30B-A3B InstructQ3_K_M · 8K~16 GB · 72–146 tok/s estimate | CPU/GPU offload |
| Gemma 2 27B ITQ3_K_M · 8K~16.5 GB · 13–23 tok/s estimate | CPU/GPU offload |
| gpt-oss-120bMXFP4 · 8K~63.2 GB · 8.5–17 tok/s estimate | Possible but slow |
| Llama 4 Scout 17B-16E InstructQ3_K_M · 8K~55.7 GB · 3–5.5 tok/s estimate | Possible but slow |
| Qwen2.5 72B InstructQ3_K_M · 8K~38.2 GB · 0.9–1.7 tok/s estimate | Not recommended |
| Llama 3.3 70B InstructQ3_K_M · 8K~37 GB · 0.9–1.8 tok/s estimate | Not recommended |
| DeepSeek R1 Distill Llama 70BQ3_K_M · 8K~37 GB · 0.9–1.8 tok/s estimate | Not recommended |
| DeepSeek R1 (671B)Q8_0 · 8K~699 GB · No data | Incompatible |
| Qwen3 235B-A22BQ8_0 · 8K~247 GB · No data | Incompatible |
For each model, the highest-precision quantization that runs entirely in fast memory; otherwise the one with the best result. Speeds are ranges; "estimate" unless a benchmark exists for this exact hardware.
EstimateData updated 24 days agoSource: Run LLM Locally engine
Limits in plain words
16 GB of VRAM holds the weights and KV cache of models up to about gpt-oss-20b at Q4_K_M. Bigger models spill into system RAM and slow down sharply: generation speed is then limited by RAM bandwidth, not by the GPU.
Where to buy a RTX 4070 Ti SUPER
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Frequently asked questions
Which LLMs can the RTX 4070 Ti SUPER run?
Entirely on the GPU: Qwen3 30B-A3B (Q3_K_M), Gemma 3 27B IT (Q3_K_M), Mistral Small 3.2 24B Instruct (IQ4_XS), gpt-oss-20b (MXFP4), Qwen3 14B (Q6_K), DeepSeek R1 Distill Qwen 14B (Q6_K), Qwen2.5 14B Instruct (Q6_K), Phi-4 14B (Q6_K). Larger models need CPU/GPU offload.
Can the RTX 4070 Ti SUPER run a 70B model?
Not recommended: a 70B model at Q4_K_M needs about 45.1 GB; the RTX 4070 Ti SUPER has 16 GB.
How is the speed estimated?
Token generation reads every active weight once per token, so it is limited by memory bandwidth (672 GB/s here). We apply an efficiency range per backend and label the result as an estimate until real benchmarks exist.