Can the RTX 4070 SUPER Run Llama 3.3 70B?
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Not entirely. Llama 3.3 70B at Q4_K_M is a 42.5 GB download (Hugging Face file listing for bartowski/Llama-3.3-70B-Instruct-GGUF), about 30.5 GB more than the RTX 4070 SUPER's 12 GB, so the remainder has to be offloaded to system RAM. Generation speed is then set by system-memory and PCIe bandwidth rather than by the GPU, so no tokens-per-second figure is quoted for this pair. The smallest card in this matrix that holds it entirely is the RTX A6000 48GB (48 GB).
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Published runs for Llama 3.3 70B on the RTX 4070 SUPER
No published run for this pair has passed the checks yet.
More about this pair
How much VRAM does Llama 3.3 70B need?
The Q4_K_M file is 42.5 GB. Add roughly 1.5 GB for the runtime and an ~8K-token context, so about 44 GB of VRAM holds it comfortably. Longer contexts grow the KV cache and need more.
What speed does Llama 3.3 70B reach on the RTX 4070 SUPER?
No published run of Llama 3.3 70B on the RTX 4070 SUPER has been cited yet. Generation reads every weight once per token, so the RTX 4070 SUPER's 504 GB/s of memory bandwidth divided by the 42.52 GB file puts the theoretical ceiling near 12 tokens per second once the whole model is resident. That is an upper bound, not a speed estimate: kernel, launch and compute overhead keep real runs below it.
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Llama 3.3 70B on other GPUs
- RTX 4060 — Offload
- RTX 3060 Ti — Offload
- RTX 3060 12GB — Offload
- Intel Arc B580 — Offload
- RTX 4060 Ti 16GB — Offload
- RTX 5060 Ti 16GB — Offload
- Intel Arc A770 16GB — Offload
- Radeon RX 9070 XT — Offload
- RTX 4070 Ti SUPER — Offload
- RTX 4080 — Offload
- RTX 5070 Ti — Offload
- RTX 5080 — Offload
- Tesla P40 24GB — Offload
- RTX 3090 — Offload
- Radeon RX 7900 XTX — Offload
- RTX 4090 — Offload
- RTX 5090 — Offload
- RTX A6000 48GB — Runs, 13.6 tok/s (1 run)
- RTX PRO 5000 Blackwell 48GB — Runs, 25.1 tok/s median
- Instinct MI210 64GB — Runs
- RTX PRO 6000 Blackwell 96GB — Runs, 30.9 tok/s median
Other models on the RTX 4070 SUPER
- Llama 3.2 1B — Runs, 183 tok/s (1 run)
- Llama 3.2 3B — Runs
- Mistral 7B — Runs
- Qwen2.5 7B — Runs
- Llama 3.1 8B — Runs, 53.4 tok/s median
- DeepSeek-R1-Distill-Llama 8B — Runs
- Qwen3 8B — Runs, 56.2 tok/s median
- Gemma 2 9B — Runs
- Gemma 3 12B — Runs
- Qwen2.5 14B — Runs, 24.9 tok/s (1 run)
- DeepSeek-R1-Distill-Qwen 14B — Runs
- Qwen3 14B — Runs, 41.4 tok/s median
- Phi-4 14B — Runs
- gpt-oss-20b — Offload
- Gemma 3 27B — Offload
- Gemma 2 27B — Offload
- Qwen3 30B-A3B — Offload
- Qwen3 32B — Offload
- Qwen2.5 32B — Offload
- DeepSeek-R1-Distill-Qwen 32B — Offload
- Qwen3 32B (Q6_K) — Offload
- Qwen2.5 32B (Q6_K) — Offload
- DeepSeek-R1-Distill-Qwen 32B (Q6_K) — Offload
- Gemma 3 27B (Q8_0) — Offload
- Llama 3.1 70B — Offload
- DeepSeek-R1-Distill-Llama 70B — Offload
How the checks work
The fit is the Q4_K_M file size against the card's VRAM, with 1.5 GB kept for the runtime and an ~8K context. A published run counts only if the pair fits, the run is Q4-class, its source does not call it an estimate, it is under the bandwidth ceiling (memory bandwidth ÷ GB read per token: the whole file for a dense model, only the active experts' weights for a mixture-of-experts model such as gpt-oss-20b or Qwen3 30B-A3B, which is why those can post figures above bandwidth ÷ file size), it is not slower than the same card on a model twice its size, a single run is not slower than the same card on a larger dense model, and it is not one figure pasted across many cards. The whole matrix is downloadable as a CSV under CC BY 4.0.
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This page is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.