What can an RTX 5070 12GB run?
The desktop GeForce RTX 5070 has 12 GB of GDDR7 on a 192-bit bus, with 672 GB/s of memory bandwidth.[1][2] The table below checks every language model in our calculator at three quantizations with an 8K-token context, using the same file sizes and fit rule as the rest of the site.
Best model for the RTX 5070 12GB: Qwen2.5 14B Instruct. It is the largest model in our calculator (14.8B parameters) that fits comfortably at 4-bit: Q4_K_M plus an 8K-token FP16 KV cache needs 10.6 GB, under 90% of 12 GB. The highest precision it fits comfortably at is 4-bit (Q4_K_M). In total, 23 of the 50 calculator models fit comfortably at 4-bit on this card.
| Model | 4-bit (Q4_K_M) | 8-bit (Q8_0) | 16-bit (FP16/BF16) |
|---|---|---|---|
| Qwen2.5 14B InstructAlibaba Qwen | 10.6 GB · Fits | 17.3 GB · Too big | 31.2 GB · Too big |
| Qwen2.5-Coder 14BAlibaba Qwen | 10.6 GB · Fits | 17.3 GB · Too big | 31.2 GB · Too big |
| Qwen3 14BAlibaba Qwen | 10.3 GB · Fits | 17.0 GB · Too big | 30.9 GB · Too big |
| Phi-4 14BMicrosoft | 10.7 GB · Fits | 17.3 GB · Too big | 31.0 GB · Too big |
| Gemma 4 12BGoogle | 7.8 GB · Fits | 13.3 GB · Too big | 24.5 GB · Too big |
| Qwen3.5 9BAlibaba Qwen | 6.4 GB · Fits | 10.1 GB · Fits | 18.7 GB · Too big |
| Ornith 1.5 9BOrnith AI | 6.0 GB · Fits | 10.1 GB · Fits | 18.7 GB · Too big |
| DeepSeek-R1-0528-Qwen3-8BDeepSeek | 6.2 GB · Fits | 9.9 GB · Fits | 17.6 GB · Too big |
| Qwen3 8BAlibaba Qwen | 6.2 GB · Fits | 9.9 GB · Fits | 17.6 GB · Too big |
| Llama 3.1 8B InstructMeta | 6.0 GB · Fits | 9.6 GB · Fits | 17.1 GB · Too big |
| Gemma 4 E4BGoogle | 5.6 GB · Fits | 8.2 GB · Fits | 15.2 GB · Too big |
| Nemotron 3.5 Bolt E4BNVIDIA | 6.4 GB · Fits | 8.4 GB · Fits | 15.2 GB · Too big |
| Qwen2.5 7B InstructAlibaba Qwen | 5.2 GB · Fits | 8.6 GB · Fits | 15.7 GB · Too big |
| Qwen2.5-Coder 7BAlibaba Qwen | 5.2 GB · Fits | 8.6 GB · Fits | 15.7 GB · Too big |
| Mistral 7B Instruct v0.3Mistral AI | 5.4 GB · Fits | 8.8 GB · Fits | 15.6 GB · Too big |
| Spark-X2.6 4BXHToken | 3.8 GB · Fits | 4.7 GB · Fits | 8.5 GB · Fits |
| Qwen3 4BAlibaba Qwen | 3.7 GB · Fits | 5.5 GB · Fits | 9.3 GB · Fits |
| Llama 3.2 3B InstructMeta | 3.0 GB · Fits | 4.4 GB · Fits | 7.4 GB · Fits |
| Qwen3 1.7BAlibaba Qwen | 2.2 GB · Fits | 3.1 GB · Fits | 5.0 GB · Fits |
| LFM3 1.7BLiquid AI | 1.2 GB · Fits | 1.9 GB · Fits | 3.6 GB · Fits |
| Qwen2.5 1.5B InstructAlibaba Qwen | 1.2 GB · Fits | 1.9 GB · Fits | 3.3 GB · Fits |
| Qwen3 0.6BAlibaba Qwen | 1.4 GB · Fits | 1.7 GB · Fits | 2.4 GB · Fits |
| Qwen2.5 0.5B InstructAlibaba Qwen | 0.5 GB · Fits | 0.6 GB · Fits | 1.1 GB · Fits |
| gpt-oss-20bOpenAI | 11.9 GB · Tight | 12.3 GB · Too big | 14.0 GB · Too big |
| Kimi K3Moonshot AI | 1,508.9 GB · Too big | 1,561.4 GB · Too big | n/a |
| Mistral Large 4 (Le Chonk)Mistral AI | 635.5 GB · Too big | 1,115.8 GB · Too big | 2,099.5 GB · Too big |
| DeepSeek-V4.1-Flash-1017DeepSeek | 500.7 GB · Too big | 508.4 GB · Too big | n/a |
| DeepSeek-V3.2DeepSeek | 406.0 GB · Too big | 713.9 GB · Too big | 1,342.9 GB · Too big |
| GLM-5.3-FlashZ.ai (Zhipu AI) | 181.5 GB · Too big | 341.1 GB · Too big | 641.8 GB · Too big |
| Qwen3-235B-A22BAlibaba Qwen | 143.7 GB · Too big | 251.5 GB · Too big | 471.9 GB · Too big |
| Qwen3.8 Flash NextAlibaba Qwen | 119.8 GB · Too big | 188.5 GB · Too big | 354.2 GB · Too big |
| Ornith-1.5-122BOrnith | 79.9 GB · Too big | 132.8 GB · Too big | 249.6 GB · Too big |
| Qwen3.5-122B-A10BAlibaba Qwen | 76.7 GB · Too big | 130.1 GB · Too big | 244.5 GB · Too big |
| gpt-oss-120bOpenAI | 63.1 GB · Too big | 63.7 GB · Too big | 65.7 GB · Too big |
| Nemotron 3.5 Bolt 74B-A9BNVIDIA | 50.5 GB · Too big | 79.1 GB · Too big | 148.5 GB · Too big |
| Llama 3.1 70B InstructMeta | 45.2 GB · Too big | 77.7 GB · Too big | 143.8 GB · Too big |
| Llama 3.3 70B InstructMeta | 45.2 GB · Too big | 77.7 GB · Too big | 143.8 GB · Too big |
| Mixtral 8x7B Instruct v0.1Mistral AI | 27.5 GB · Too big | 50.7 GB · Too big | 94.5 GB · Too big |
| Recursive FlintRecursive | 25.3 GB · Too big | 42.5 GB · Too big | 79.8 GB · Too big |
| Qwen3.6 35B-A3BAlibaba Qwen | 22.5 GB · Too big | 38.0 GB · Too big | 71.2 GB · Too big |
| Qwen3.8-35B-A3B (Empero Distill)Empero | 21.9 GB · Too big | 38.0 GB · Too big | 71.2 GB · Too big |
| Nanbeige4.3 33BNanbeige | 22.5 GB · Too big | 36.9 GB · Too big | 68.0 GB · Too big |
| Qwen2.5 32B InstructAlibaba Qwen | 22.0 GB · Too big | 37.0 GB · Too big | 67.7 GB · Too big |
| Qwen2.5-Coder 32BAlibaba Qwen | 22.0 GB · Too big | 37.0 GB · Too big | 67.7 GB · Too big |
| Qwen3 32BAlibaba Qwen | 21.9 GB · Too big | 37.0 GB · Too big | 67.7 GB · Too big |
| Gemma 4 31BGoogle | 21.5 GB · Too big | 34.6 GB · Too big | 63.3 GB · Too big |
| Qwen3.8 27BAlibaba Qwen | 18.0 GB · Too big | 29.7 GB · Too big | 55.2 GB · Too big |
| Gemma 4 26B A4BGoogle | 17.5 GB · Too big | 27.3 GB · Too big | 51.0 GB · Too big |
| Devstral Small 2 24BMistral AI | 15.7 GB · Too big | 26.4 GB · Too big | 48.5 GB · Too big |
| LFM2.5-24B-A2BLiquid AI | 14.7 GB · Too big | 25.7 GB · Too big | 48.1 GB · Too big |
Each cell is the Hugging Face GGUF file size plus FP16 KV cache for an 8K-token context (or the model's maximum, if shorter). Fits = under 90% of 12 GB; Tight = 90 to 100%; Too big = over 12 GB. * = KV cache not included (no config data). Same data as the VRAM calculator and what can I run with 12 GB. Data checked 2026-11-03.
Check one model at your own context length in the VRAM calculator with the RTX 5070 selected.
Same 12 GB ceiling as an RTX 3060 12GB
Memory decides what fits, and both cards have 12 GB, so the RTX 5070 fits exactly the same models as an RTX 3060 12GB. At 4-bit with an 8K context, the biggest that fit comfortably are 14B-class models such as Qwen3 14B (about 10.3 GB) and Phi-4 (about 10.7 GB). gpt-oss-20b needs about 11.9 GB, 99% of the card: it may load, but it's tight. Anything bigger needs offloading or a bigger card. What the 5070 changes is speed. Specs from NVIDIA:[1][2][3]
| RTX 5070 | RTX 3060 12GB | |
|---|---|---|
| Memory | 12 GB GDDR7, 192-bit | 12 GB GDDR6, 192-bit (an 8 GB version also exists) |
| Memory bandwidth | 672 GB/s | 360 GB/s |
| CUDA cores | 6,144 | 3,584 |
| AI TOPS (NVIDIA's figure) | 988 | 102 |
| Architecture | Blackwell | Ampere |
| Power (NVIDIA spec) | 250 W, 650 W system power | 170 W |
Generating text mostly reads the weights from memory, so bandwidth is the number to watch: the 5070 has 87% more (our arithmetic). Reading a long prompt is limited by compute, where the gap is bigger.
How much faster? Measured llama.cpp results
llama.cpp's CUDA performance thread keeps a scoreboard of one small test model (Llama 2 7B at Q4_0) run with llama-bench. These two rows were posted by different people on different systems and llama.cpp builds, so treat the gap as a rough guide, not a lab comparison:[4]
| Llama 2 7B Q4_0, llama.cpp CUDA | RTX 5070 12 GB | RTX 3060 12 GB | 5070 lead |
|---|---|---|---|
| Generation (tg128) | 127.54 tok/s | 75.57 tok/s | +69% |
| Reading a 512-token prompt (pp512) | 5,184.75 tok/s | 2,137.50 tok/s | +143% |
The lead column is our arithmetic. The generation lead (+69%) is a bit under the bandwidth lead (+87%), which is typical: no card turns all of its bandwidth into tokens. Bigger models generate more slowly on both cards, roughly in proportion to their file size. We haven't found a sourced RTX 5070 run of a larger model, so we don't publish one; more cards are on the measured speed page.
In short: if you already own an RTX 3060 12GB, a 5070 adds speed but no new models. If you're buying for local AI, the next question is whether you want speed or room.
RTX 5070 12GB vs RTX 5060 Ti 16GB
These two are the usual choice at this level, and they trade off differently:
- The RTX 5060 Ti 16GB fits more. 4 GB more memory takes gpt-oss-20b from tight to comfortable and adds 4-bit models up to about 14 GB with an 8K context. See the RTX 5060 Ti 16GB fit table.
- The RTX 5070 is faster on what both can fit. It has 672 GB/s of bandwidth against the 5060 Ti's 448 GB/s.[2] In the same test in that thread, a 5060 Ti 16GB generated 90.94 tok/s (build 89d10295) against the 5070's 127.54 tok/s, again from different systems.[4]
For local language models, memory usually matters more than speed: a model that doesn't fit runs far slower than any card difference. If you mainly run 7B to 14B models and want them fast, the 5070 is the quicker card. The RTX 5070 Ti has 16 GB and 896 GB/s, so it gives you both.[1][2] All 12 GB and 16 GB options are compared on best GPUs for local LLMs: 12 GB and 16 GB.
The laptop RTX 5070 is a different card
NVIDIA lists the RTX 5070 Laptop GPU with 12 GB or 8 GB of GDDR7 and 384 GB/s, about 57% of the desktop card's bandwidth (our arithmetic).[5] So a laptop with the 8 GB version fits far fewer models (see what fits in 8 GB), and even the 12 GB version will generate more slowly than the desktop card. Check the spec sheet for the memory size. The RTX 5070 Ti Laptop GPU is listed with 12 GB at 672 GB/s, the same memory size and bandwidth as the desktop RTX 5070.[5]
More for 12 GB cards
- Everything that fits in 12 GB, including image generation and gaming
- RTX 3060 12GB fit table, the same list for the older card
- Best GPUs for local LLMs: 12 GB, with specs from NVIDIA and AMD
- Run a MoE model bigger than your VRAM with expert offload to system RAM
- CUDA out of memory? What to change first
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Sources
All sources checked on .
- NVIDIA, GeForce RTX 5070 family specs (RTX 5070: 12 GB GDDR7, 192-bit, 6,144 CUDA cores, 988 AI TOPS, 250 W total graphics power, 650 W required system power; RTX 5070 Ti: 16 GB GDDR7, 256-bit). Checked 2026-11-03. https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5070-family/
- NVIDIA, RTX 50 Series dynamic compare table ("Memory Bandwidth": RTX 5070 672 GB/sec, RTX 5070 Ti 896 GB/sec, RTX 5060 Ti 448 GB/sec, RTX 3060 360 GB/sec; RTX 3060 102 AI TOPS, Ampere). Checked 2026-11-03. https://images.nvidia.com/aem-dam/en-zz/Solutions/raw-html/dynamic-specs-table-50-series/v1.1.3.js
- NVIDIA, GeForce RTX 3060 family specs (RTX 3060: 12 GB or 8 GB GDDR6, 192-bit, 3,584 CUDA cores, 170 W). Checked 2026-11-03. https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3060-3060ti/
- llama.cpp GitHub discussion #15013, "Performance of llama.cpp on Nvidia CUDA": scoreboard rows for RTX 5070 12 GB (posted by @Spyro000) and RTX 3060 12 GB (posted by @QuantiusBenignus, build baa9255), and the RTX 5060 Ti 16 GB result by @mike-llamacpp (build 89d10295); Llama 2 7B Q4_0,
llama-benchpp512 / tg128. Checked 2026-11-03. https://github.com/ggml-org/llama.cpp/discussions/15013 - NVIDIA, GeForce RTX 50 Series laptops (RTX 5070 Laptop GPU: 12 GB or 8 GB GDDR7, 384 GB/s; RTX 5070 Ti Laptop GPU: 12 GB GDDR7, 672 GB/s). Checked 2026-11-03. https://www.nvidia.com/en-us/geforce/laptops/50-series/
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