Best GPU for local LLMs in 2026
Choose a GPU for your model, context and concurrency. Check memory, runtime support and multi-GPU limits, then inspect submitted local short-chat examples.
6 reported GPU-host configurations have usable local short-chat examples in the loaded sample. Each row shows the newest eligible submission for that reported configuration, ordered by name. Different models, quantizations and settings prevent a hardware speed ranking or answer-quality recommendation.
Choose the model file, context and number of simultaneous requests first. Then check runtime support and measure that workload before buying another card.
Start with the workload you need to run. A model loading successfully does not establish useful response time or answer quality. The examples below show reported configurations and source settings; use them to find relevant measurements, then validate your intended model, quantization and workload before buying.
Three checks before buying or adding a GPU
1. Can the runtime use this exact card?
Check the GPU, operating system, driver and runtime together in the Ollama hardware support list. A supported vendor name does not establish support for every card or mixed-card setup. Check motherboard slots, physical clearance, cooling and power requirements against the manufacturers' specifications too.
2. Does the full workload fit?
Budget for the exact weight file, context cache, runtime allocations and other resident models. Two simultaneous long conversations need a different memory budget from one short chat. Ollama documents how parallel requests increase context allocation. Start with the memory estimator, then measure your actual concurrency; its estimate is not a guarantee.
3. Is a second card solving the right problem?
Two cards do not behave like one card merely because their capacities add up. Ollama prefers one GPU when the model fits, otherwise it can split across available GPUs. llama.cpp exposes device and split controls. Validate support and placement on each device; more capacity does not establish a speed increase. System RAM plus VRAM is also a different configuration from GPU-only inference. In Ollama, inspect ollama ps and follow the placement and latency checklist.
Before spending, run representative prompts at your intended context and concurrency. Record first-token wait, decode speed, memory use and whether the answers meet your task. Our Qwen3.8 and Gemma 4 RTX 5090 study supplies specific speed measurements; it does not validate a different GPU pair, long-context concurrency or coding quality.
Sources checked September 22, 2026. The examples below retain reported host capacities, including nonstandard configurations. They do not prove GPU-only residency, current prices or which purchase has the best value. Token counts describe measured prompts and responses, not the maximum configured context. Missing fields remain explicit.
Submitted benchmarks
| Reported host | Model and settings | Decode | Source |
|---|---|---|---|
| RTX 3090 (24GB) + AMD EPYC 7663 56-Core Processor (56c) + 252GB | llama3.1 Q4_K_M · ollama 0.31.1 Prompt / output tokens: 110 / 256 TTFT: 49,959.98 ms | 136.2tok/s | r_9hurqggbshk Submitted 2026-07-02 |
| RTX 3090 (24GB) + AMD EPYC 7702P 64-Core Processor (64c) + 252GB | deepseek-coder-v2 Q4_0 · ollama 0.31.1 Prompt / output tokens: 117 / 256 TTFT: 734.851 ms | 189.5tok/s | r_o2-1w665rtq Submitted 2026-07-02 |
| RTX 4090 (24GB) + AMD EPYC 7352 24-Core Processor (24c) + 252GB | qwen3-coder Q4_K_M · ollama 0.31.1 Prompt / output tokens: 110 / 256 TTFT: 562.272 ms | 145.2tok/s | r_wjq32z47vlp Submitted 2026-07-02 |
| RTX 4090 (24GB) + AMD EPYC 7443 24-Core Processor (24c) + 252GB | gemma3 Q4_K_M · ollama 0.31.1 Prompt / output tokens: 118 / 256 TTFT: 816.561 ms | 46.95tok/s | r_x23y_sg24pm Submitted 2026-07-02 |
| RTX 4090 (48GB) + AMD EPYC 7763 64-Core Processor (128c) + 1008GB | qwen2.5-coder Q4_K_M · ollama 0.31.1 Prompt / output tokens: 131 / 256 TTFT: 280.772 ms | 89.45tok/s | r_73tnfdueq2h Submitted 2026-07-02 |
| RTX 5090 (32GB) + AMD Ryzen 7 9850X3D 8-Core Processor (8c) + 30GB | gemma-4-12b-it-qat Q4_0 · llama.cpp 1 (9725a31) Prompt / output tokens: 121 / 256 TTFT: 59.667 ms | 141.1tok/s | r_v5arqmazf31 Submitted 2026-09-05 |
See also: All hardware · All models · Methodology