Best GPUs for Local LLMs (2026)
The best GPU for local LLM inference in 2026 is the GIGABYTE RTX 5070 WINDFORCE OC — its Blackwell architecture with 672 GB/s GDDR7 bandwidth delivers 60–100 tokens/sec on Llama 3.1 8B via CUDA, with zero configuration friction on Windows or Linux. For users who want to run 13B models at Q8 precision or 14B models without hitting VRAM limits, the GIGABYTE RX 9060 XT 16G offers 4GB more headroom at a similar price — provided you're on Linux with ROCm.
Ranked Picks
3 reviewed01
Top Pick
GIGABYTE GeForce RTX 5070 WINDFORCE OC 12G
Top pick for LLM inference. 12GB GDDR7 at 672 GB/s delivers fast tokens/sec on 7B–13B models. CUDA support means Ollama, LM Studio, and llama.cpp all work out-of-the-box on Windows and Linux. Blackwell's 5th-Gen Tensor Cores improve throughput over Ada Lovelace. Best overall GPU if you want performance with zero setup friction.
02
ASUS Prime GeForce RTX 5070 SFF-Ready 12GB
Best LLM GPU for custom compact builds. Full RTX 5070 performance in a 2.5-slot SFF card — pair with a Mini-ITX system for a powerful, small-footprint local AI workstation. Same 12GB GDDR7 and CUDA ecosystem as the WINDFORCE. Ideal for users building a dedicated private AI server that doesn't take over the desk.
03
GIGABYTE Radeon RX 9060 XT GAMING OC 16G
Best for large model headroom. 16GB GDDR6 fits 13B models at Q8 and 14B models at Q4 entirely in VRAM — avoiding the CPU offload penalty that kills throughput on 12GB cards. Trade-off: 288 GB/s GDDR6 bandwidth means slower tokens/sec than the RTX 5070. ROCm on Linux is required for GPU acceleration. Best for Linux users running larger models who prioritize VRAM over raw speed.
Hardware Requirements
Minimum 8GB VRAM for 7B models (Q4 quantization). 12GB for 13B models at Q4. 16GB for 13B models at Q8 or 14B models at Q4 without CPU offload.
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, 8GB is the comfortable minimum. 12GB gives headroom for system prompts and longer contexts. Both RTX 5070 variants (12GB) run Llama 3.1 8B entirely in VRAM with room to spare.
Q2How fast is the RTX 5070 for local LLMs compared to older cards?
The RTX 5070 delivers approximately 60–100 tokens/sec on Llama 3.1 8B via CUDA with Ollama — a 30–50% improvement over the RTX 4070 Super thanks to Blackwell's Tensor Core improvements and GDDR7 bandwidth. For 13B Q4 models, expect 30–55 tokens/sec — fast enough for interactive chat.
Q3Does the RX 9060 XT work with Ollama on Windows?
Partially. Ollama supports AMD GPUs via ROCm, but Windows ROCm support is less mature than Linux. Some models may fall back to CPU inference if ROCm isn't correctly detected. On Linux with ROCm 6.x, the RX 9060 XT runs fully GPU-accelerated. For Windows users who want plug-and-play LLM inference, the RTX 5070 WINDFORCE is the safer choice.
Q4Is a dedicated GPU faster than a Mac Mini M4 Pro for LLMs?
For models that fit in VRAM, yes — the RTX 5070 at 672 GB/s is faster than the M4 Pro at 273 GB/s for 7B–13B models. However, the Mac Mini M4 Pro with 48GB+ unified memory can run larger models without the VRAM ceiling. For 7B–13B workloads, the RTX 5070 wins on speed. For 30B+ models, Apple Silicon wins on capacity.
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