Best GPUs for Local LLMs (2026)
The best GPU for running local LLMs in 2026 is the NVIDIA RTX 4090 — its 24GB GDDR6X VRAM and 1,008 GB/s bandwidth deliver 70–90 tokens/sec on Llama 3.1 8B and can fit quantized 70B models end-to-end in a single card. For users who want strong 7B–34B performance without the price premium, the RTX 4070 Super's 12GB and 504 GB/s bandwidth hit 40–55 tokens/sec on 8B models — fast enough for real-time chat.
Ranked Picks
3 reviewed01
Top Pick
NVIDIA GeForce RTX 4090 24GB
Top pick for LLM inference. 24GB VRAM fits Llama 3.1 70B at Q4_K_M quantization (39GB) when combined with system RAM via llama.cpp. Native CUDA support works out-of-the-box with Ollama, LM Studio, and llama.cpp. Delivers 70–90 tok/s on 8B models.
02
NVIDIA GeForce RTX 4070 Super 12GB
Best value GPU for LLMs. 12GB VRAM fits 7B–13B models fully in VRAM at Q4/Q5 quantization. CUDA support identical to the 4090 — the same Ollama models just work. Delivers 40–55 tok/s on Llama 3.1 8B. Cannot fit 70B models without CPU offloading.
03
AMD Radeon RX 7900 XTX 24GB
Best AMD GPU for LLMs on Linux. 24GB VRAM matches the RTX 4090's capacity. ROCm support in llama.cpp and Ollama is production-grade on Ubuntu 22.04+. Delivers 60–75 tok/s on 8B models — within 15% of the 4090. Windows support via DirectML is functional but slower.
Hardware Requirements
Minimum 8GB VRAM for 7B models (Q4 quantization). 12GB for 13B models. 24GB to fit 70B models at Q4_K_M without CPU offloading — below that you'll see significant speed drops from memory bandwidth penalties.
Why This Matters
Tokens-per-second scales almost linearly with memory bandwidth for LLMs. A GPU with more bandwidth generates faster, more fluid responses. VRAM capacity determines which model sizes fit entirely in GPU memory — once a model exceeds VRAM, layers spill to system RAM over the PCIe bus, dropping throughput by 10–100× depending on how much overflows.
Frequently Asked Questions
Q1How much VRAM do I need for Llama 3.1 8B?
Llama 3.1 8B at Q4_K_M quantization requires approximately 5–6GB VRAM. With context overhead, a 6GB GPU will run it but may truncate long contexts. 8GB is the comfortable minimum — it fits the model and a 4K token context without swapping. 12GB gives headroom for system prompts and parallel requests.
Q2Can I run Llama 3 70B on a single GPU?
Yes, on the RTX 4090 or RX 7900 XTX with 24GB VRAM, using Q4_K_M quantization (~39GB total). At 24GB, llama.cpp will offload roughly 40% of layers to CPU RAM — you'll get 5–15 tokens/sec rather than the 10–20 tok/s with all layers in GPU VRAM. For full GPU inference of 70B, you need multiple GPUs or a Mac with 96GB unified memory.
Q3Do AMD GPUs work with Ollama?
Yes. Ollama added official ROCm support in 2024. On Linux with ROCm 6.x, the RX 7900 XTX runs all Ollama models at near-CUDA performance. Windows support exists via DirectML but performance is 30–50% lower. If you're on Windows and want AMD, consider a Mac Mini M4 Pro instead — Apple Silicon's unified memory architecture is often faster than AMD GPU inference on Windows.
Q4Is a GPU faster than Apple Silicon for local LLMs?
For models that fit in VRAM: yes, high-end NVIDIA GPUs are faster. The RTX 4090 delivers 70–90 tok/s on 8B vs 30–45 tok/s on a Mac Mini M4 Pro. However, for models that exceed GPU VRAM (like 70B), Apple Silicon's unified memory architecture wins — 96GB of unified memory at 273 GB/s beats a GPU that has to offload to slow system RAM.
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