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Best GPUs for Local LLMs 2026, by Model Size
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items Graphics cards for running open models like Qwen, Llama and gpt-oss at home, grouped by the biggest model each one can hold. VRAM decides what fits; bandwidth decides how fast it runs. Every note cites a named benchmark (Hardware Corner, Puget System...
Sections
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8B to 14B models: 12 to 16GB on a budget
Fast 16GB for coding assistants
32B models: 24 to 32GB
Build essentials for a local AI PC
70B to 120B models: 48 to 96GB
8B to 14B models: 12 to 16GB on a budget
The cheapest cards that run 8B models fast and 14B models at 4-bit.
Cheapest way to 12GB of VRAM for 8B models; Compute Market measured Llama 3 8B at 4-bit at 28 tokens/sec on Linux with IPEX-LLM. Expect more setup than NVIDIA. Source: Compute Market.
ASRock Arc B580 12GB
Budget 16GB card that runs 14B coding models at 4-bit; community llama.cpp results show Qwen2.5-Coder 14B at about 33.5 tokens/sec on Vulkan. Its 320 GB/s bandwidth limits speed. Source: llamaperf.
Radeon RX 9060 XT 16GB (Gigabyte)
Hardware Corner's best budget GPU for local LLMs: 51 tokens/sec on Qwen3 8B and 82 on gpt-oss 20B at 4-bit. Buy the 16GB version, not the 8GB, and two of them give 32GB. Source: Hardware Corner.
RTX 5060 Ti 16GB (PNY)
Fast 16GB for coding assistants
Same 16GB ceiling, much faster replies and prompt reading for coding agents.
Best speed per dollar at 16GB: Hardware Corner measured 87.5 tokens/sec on Qwen3 8B and 133 on gpt-oss 20B, close to the pricier RTX 5080. Source: Hardware Corner.
RTX 5070 Ti 16GB (PNY)
AMD's fastest 16GB option for 8B to 14B models: about 99 tokens/sec on Qwen3 8B and 54 on Qwen3 14B under ROCm in community tests. Use ROCm, since a Vulkan bug can slow big models. Source: llamaperf.
Radeon RX 9070 XT 16GB (PowerColor)
Fastest 16GB card Hardware Corner tested: 94 tokens/sec on Qwen3 8B and 64 on Qwen3 14B. It still can't hold a dense 32B model with useful context. Source: Hardware Corner.
RTX 5080 16GB (Gigabyte)
32B models: 24 to 32GB
Enough VRAM to keep dense 27B to 32B models, like Qwen3 32B, fully on the card.
The r/LocalLLaMA classic: Hardware Corner calls a used 3090 the best value for VRAM, with 24GB for 30B to 32B models and 87 tokens/sec on Qwen3 8B. Source: Hardware Corner.
RTX 3090 24GB (Refurbished)
32GB keeps dense 27B to 32B models fully in VRAM for less than an RTX 5090, though slower; Puget Systems tested it for AI inference. Source: Puget Systems.
Radeon AI PRO R9700 32GB (ASRock)
The fastest consumer card for 32B models: 51 tokens/sec on dense Qwen3 32B at 4-bit with 16K context, and 145 on Qwen3 8B. A 70B model at 4-bit (~43GB) needs two. Source: Hardware Corner; Presenc.
RTX 5090 32GB (ASUS)
AMD's 24GB card with 960 GB/s of bandwidth; llamaperf's community runs show about 48 tokens/sec on a 27B model at 4-bit, well ahead of other 24GB new cards. Works well with Ollama on Linux via ROCm. Source: llamaperf; LLM Configurator.
Radeon RX 7900 XTX 24GB (Sapphire)
The cheapest new 24GB card; SpecPicks reports about 19 tokens/sec on Qwen3 32B at 4-bit, roughly 25-35% behind a used RTX 3090 but with a warranty. Expect IPEX-LLM or SYCL setup work. Source: SpecPicks; llamaperf.
Arc Pro B60 24GB (ASRock)
Build essentials for a local AI PC
The parts that keep a big-GPU build stable and load models fast.
Covers RTX 5080 and 5070 Ti builds with headroom, and has the native 12V-2x6 cable; Tom's Hardware says 1000W is NVIDIA's minimum for a 5090. Source: Tom's Hardware.
CORSAIR RM1000x (2024, ATX 3.1)
Holds several 20 to 40GB model files and loads them fast; Tom's Hardware's premium PCIe 5.0 pick, rated up to 14,700 MB/s reads. Source: Tom's Hardware.
Samsung 9100 PRO 2TB
70B to 120B models: 48 to 96GB
Workstation cards for 70B dense and 120B mixture-of-experts models on a single GPU.
96GB on one card fits 70B models and 120B-class MoE models like gpt-oss 120B at 4-bit; llamaperf lists it second only to the RTX 5090 on 27B speed. A workstation card priced near $9,000+. Source: llamaperf; LLM Configurator.
RTX PRO 6000 Blackwell 96GB (PNY)
48GB on one 300W, two-slot card is enough for a 70B model at 4-bit (about 44GB), which a single RTX 5090 can't hold. LLM Configurator estimates 94 to 194 tokens/sec on Llama 3.1 8B from its bandwidth. Source: LLM Configurator.
RTX PRO 5000 Blackwell 48GB (PNY)