# gpt-oss-120b vs Llama-3.3-70B-Instruct | What Can I Run AI? > gpt-oss-120b (~82.5 GB at Q4) vs Llama-3.3-70B-Instruct (~50.2 GB): memory at Q4/Q8/FP16, context cache, cheapest GPU and Mac, and est. tok/s side by side. Source: https://whatcanirunai.vercel.app/compare/gpt-oss-120b-vs-llama-3.3-70b-instruct/ # gpt-oss-120b vs Llama-3.3-70B-Instruct: VRAM and speed for running locally Side by side: how much memory each model needs at Q4, Q8 and FP16, how the KV cache grows with context, the cheapest GPU and Mac that run each, and estimated speed on 11 common machines. **Llama-3.3-70B-Instruct** is lighter: about 50.2 GB at Q4 against 82.5 GB for gpt-oss-120b, though both run on 7 of the devices we track. **gpt-oss-120b** should be faster on the same hardware, since it reads 5.1B parameters per token against 70.6B (it is a mixture-of-experts model). Which one gives better answers depends on your task, so check benchmarks for that; this page covers what it takes to run them. __gpt-oss-120b vs Llama-3.3-70B-Instruct: specs and memory__ | [gpt-oss-120b](https://whatcanirunai.vercel.app/models/gpt-oss-120b/) | [Llama-3.3-70B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.3-70b-instruct/) | | | ------------------------------------- | -------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | | Maker | openai | meta-llama | | License | apache-2.0 | llama3.3 | | Type | Mixture of experts | Dense | | Total parameters | 116.8B | 70.6B | | Active per token | 5.1B | 70.6B all | | Memory at Q4\_K\_M | **82.5 GB** | **50.2 GB** | | Memory at Q8\_0 | **143.8 GB** | **87.2 GB** | | Memory at FP16 | **269.7 GB** | **163.3 GB** | | KV cache growth | 74 MB per 1k tokens, FP16 | 328 MB per 1k tokens, FP16 | | Memory at Q4, 32k context | 83.5 GB 1.2 GB cache | 59.6 GB 10.7 GB cache | | Cheapest GPU at Q4 | [NVIDIA RTX Pro 6000 Blackwell 96GB](https://whatcanirunai.vercel.app/hardware/rtx-pro-6000-blackwell/) $8,565 | [NVIDIA RTX Pro 6000 Blackwell 96GB](https://whatcanirunai.vercel.app/hardware/rtx-pro-6000-blackwell/) $8,565 | | Cheapest Mac / mini PC at Q4 | [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/) $3,449 | [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/) $3,449 | | Cheapest GPU at Q8 | none we track | none we track | | Tracked devices that run it at Q4 | 7 of 82 | 7 of 82 | ## Estimated speed on the same hardware Decode tokens per second at Q4\_K\_M from memory bandwidth ([how we estimate](https://whatcanirunai.vercel.app/method/#speed)). MoE figures are upper bounds ("up to"). Estimates, not benchmarks. __Estimated tokens per second, gpt-oss-120b vs Llama-3.3-70B-Instruct__ | Device | gpt-oss-120b | Llama-3.3-70B-Instruct | | --------------------------------------------------------------------------------------------------------------- | ------------ | ---------------------- | | [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/)12 GB usable · 360 GB/s · $329 | doesn’t fit | doesn’t fit | | [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-16gb/)16 GB usable · 288 GB/s · $499 | doesn’t fit | doesn’t fit | | [GeForce RTX 5080](https://whatcanirunai.vercel.app/hardware/rtx-5080/)16 GB usable · 960 GB/s · $999 | doesn’t fit | doesn’t fit | | [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/)24 GB usable · 936 GB/s · $1,499 | doesn’t fit | doesn’t fit | | [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/hardware/rx-7900-xtx/)24 GB usable · 960 GB/s · $999 | doesn’t fit | doesn’t fit | | [GeForce RTX 5090](https://whatcanirunai.vercel.app/hardware/rtx-5090/)32 GB usable · 1792 GB/s · $1,999 | doesn’t fit | doesn’t fit | | [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/)18 GB usable · 170 GB/s · $1,299 | doesn’t fit | doesn’t fit | | [Mac mini M6 32GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-32/)24 GB usable · 170 GB/s · $1,799 | doesn’t fit | doesn’t fit | | [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/)48 GB usable · 307 GB/s · $3,199 | doesn’t fit | doesn’t fit | | [Framework Desktop (Ryzen AI Max+ 395) 128GB](https://whatcanirunai.vercel.app/hardware/framework-desktop-128/)96 GB usable · 256 GB/s · $3,449 | up to 66 | 3–5 | | [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/)96 GB usable · 614 GB/s · $6,699 | up to 159 | 6–11 | ## Quick answers Which needs less VRAM, gpt-oss-120b or Llama-3.3-70B-Instruct? Llama-3.3-70B-Instruct: about 50.2 GB at Q4\_K\_M, against 82.5 GB for gpt-oss-120b. At Q8\_0 it's 87.2 GB vs 143.8 GB. Which is faster locally, gpt-oss-120b or Llama-3.3-70B-Instruct? Decode speed is limited by how many bytes are read per token. gpt-oss-120b reads 5.1B parameters per token and Llama-3.3-70B-Instruct reads 70.6B, so gpt-oss-120b should be faster on the same machine. Our estimate on the Framework Desktop (Ryzen AI Max+ 395) 128GB at Q4 is up to 66 tok/s vs 3–5 tok/s. These are estimates, not benchmarks. What is the cheapest GPU for gpt-oss-120b and Llama-3.3-70B-Instruct? At Q4: NVIDIA RTX Pro 6000 Blackwell 96GB ($8,565) for gpt-oss-120b; NVIDIA RTX Pro 6000 Blackwell 96GB ($8,565) for Llama-3.3-70B-Instruct. Prices are dated list-price snapshots. Can a Mac run gpt-oss-120b and Llama-3.3-70B-Instruct? The cheapest Mac or mini PC we track for gpt-oss-120b is the Framework Desktop (Ryzen AI Max+ 395) 128GB ($3,449). For Llama-3.3-70B-Instruct it's the Framework Desktop (Ryzen AI Max+ 395) 128GB ($3,449). Which is better, gpt-oss-120b or Llama-3.3-70B-Instruct? We don't score answer quality. This comparison covers memory, context cache, hardware and speed. Pick the one that fits your hardware, then test both on your own prompts. [All comparisons](https://whatcanirunai.vercel.app/compare/) · [MoE vs dense](https://whatcanirunai.vercel.app/moe-vs-dense/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/) · [Context length and VRAM](https://whatcanirunai.vercel.app/context-length/) · [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) --- Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/