Best GPU for local AI under €2,000 in 2026

Choose the best GPU for AI under 2,000 euros is primarily about balancing available VRAM, memory bandwidth, and compatibility with open-source inference frameworks. In 2026, the RTX 4090, the RTX 5090, and the RX 7900 XTX occupy very different positions in this segment, each with its own tradeoffs for running LLMs locally. This article details each GPU's key specs, the catalog models available for each configuration, expected performance by quantization level, and answers the most frequently asked questions before purchase.

Why VRAM is the decisive metric for local LLMs

Unlike gaming or 3D rendering workloads, local LLM execution depends heavily on the video memory capacity. VRAM determines which quantization is usable and how much the model must be offloaded to system RAM.

The most common quantization formats via llama.cpp, the reference inference engine for local inference, are:

To fully understand the impacts of quantization, see the LLM quantization guide of the site.

As a reference point for the models indexed in the BestLLMfor catalog:

The direct consequence: with a single GPU under €2,000, all models in the catalog (at least 71B parameters) run in partial offload mode into system RAM, which reduces tokens/sec proportionally to the amount offloaded.

GPUs available under €2,000 in 2026

Here are the graphics cards relevant to local LLM inference in this price segment (prices observed in France in mid-2026, to be confirmed based on retailers and market fluctuations):

NVIDIA GeForce RTX 4090 - VRAM: 24 GB GDDR6X - Memory bandwidth: 1,008 GB/s - Indicative price: 1 400–1 700 € - Compatibility: native CUDA, llama.cpp, vLLM, Ollama, ExLlamaV2 - Strength: the broadest ecosystem, with extensive community documentation

NVIDIA GeForce RTX 5090 - VRAM: 32 GB GDDR7 - Memory bandwidth: estimated at ~1,790 GB/s - Indicative price: €1,900–2,200 depending on the reseller (borderline for the target budget) - Compatibility: Native CUDA, Blackwell architecture supported in recent llama.cpp - Strength: 8 GB more than the RTX 4090, reducing CPU offloading for 70B models

AMD Radeon RX 7900 XTX - VRAM: 24 GB GDDR6 - Memory bandwidth: 960 GB/s - Indicative price: 800–1 050 € - Compatibility: ROCm 6.x, llama.cpp via the HIP backend, Ollama - Strength: best VRAM-to-price ratio in the segment, viable if your stack supports ROCm

NVIDIA GeForce RTX 3090 (secondary market) - VRAM: 24 GB GDDR6X - Indicative price: 600–850 € - Strength: the same VRAM capacity as the RTX 4090 at a lower price - Weakness: slightly lower bandwidth (~936 GB/s), older architecture (Ampere)

NVIDIA GeForce RTX 5080 - VRAM: 16 GB GDDR7 - Indicative price: 900–1 100 € - Weakness: 16 GB is insufficient for 70B models even in Q3_K_M — not recommended for the catalog's LLMs

For an in-depth comparison of the two segment leaders, see the page RTX 4090 vs. RTX 5090 for LLMs.

RTX 4090 vs RTX 5090: the analysis for LLMs

The RTX 5090 adds 8 GB of VRAM and significantly higher bandwidth. For LLMs, this translates into three concrete advantages:

Reduced CPU offloading: with 32 GB of VRAM, you offload fewer layers to system RAM for a 70B model in Q4 (~42 GB required). In Q3_K_M, the footprint of a 72B model drops to approximately 30–32 GB (estimated), which may allow fully VRAM-based execution depending on the implementation.

Higher tokens/sec: less offloading means less of a bottleneck on the PCIe bus (limited to ~32 GB/s on PCIe 5.0 x16, versus 1,008 GB/s for the internal bandwidth of the RTX 4090). The difference is directly noticeable in inference speed.

Additional cost: €400 to €700 more depending on the retailer. If the RTX 5090 drops below €2,000, the 32 GB justify the extra cost. Otherwise, RTX 4090 remains the most consistent choice.

The RX 7900 XTX deserves special mention for budgets under €1,050: 24 GB of VRAM with steadily improving ROCm support. Compatibility is not yet as smooth as CUDA across all tools (especially vLLM), but llama.cpp via HIP works correctly for standard local inference.

The official specifications for the RTX 50 series are available on the NVIDIA product page.

Models accessible with a GPU under €2,000 (including offload)

Here's what's realistic from the BestLLMfor catalog, with a 24–32 GB VRAM GPU combined with 64–128 GB of DDR5 system RAM:

70B models — the reference class with partial offloading

What remains out of reach on a single GPU

To identify models filtered by your available VRAM, use the BestLLMfor configurator.

Expected performance: tokens/sec depending on the GPU

The figures below are estimated based on community benchmarks compiled from the repository's issues and discussions llama.cpp on GitHub and the Hugging Face Open LLM leaderboard. They vary depending on system RAM, inference engine version, and PCIe configuration.

For a 70B model in Q4_K_M (about 42 GB), partial offload:

For a 70B model in Q3_K_M (estimated at ~30–32 GB), close to the RTX 5090 threshold:

These figures illustrate the critical importance of the internal memory bandwidth: eliminating CPU offload doubles to quadruples inference speed compared with partial offloading.

Model licenses: what changes by use case

Available VRAM determines which models are accessible, and therefore which licenses apply to your deployment. License reminder for 70–120B-class models in the catalog:

For any commercial project, always check the exact terms on each model's page before deployment. Apache 2.0 and MIT licenses offer maximum flexibility.

FAQ

Q: Can you run a 70B model on a RTX 4090?

Yes, with partial offloading to system RAM. The RTX 4090 has 24 GB of VRAM; a model like the Qwen 2.5 72B Instruct, which requires ~42 GB in Q4, offloads about 18 GB to DDR5 RAM. Inference speed is around 4–9 tokens/sec (estimated), which remains usable for non-real-time interactive use, provided you have at least 64 GB of system RAM and a CPU with sufficient memory bandwidth.

Q: Does the RTX 5090 fit within a €2,000 budget in France?

It's borderline: between €1,900 and €2,200 depending on resellers in mid-2026. If you find it below €2,000, its 32 GB of VRAM and higher bandwidth than RTX 4090 significantly reduce CPU offloading on 70B models, improving inference speed. Otherwise, RTX 4090 remains the most accessible option and the one best supported by the local-tools ecosystem.

Q: AMD RX 7900 XTX or NVIDIA RTX 4090 for local LLMs?

The RX 7900 XTX offers 24 GB of VRAM for €800–1,050, or €600–700 less than the RTX 4090. The tradeoff is that the ROCm ecosystem is less mature than CUDA: vLLM and some backends require manual configuration, and HIP performance remains lower than CUDA in practice. For llama.cpp and Ollama in standard use, the RX 7900 XTX works properly. For a more complex stack or application deployment, the RTX 4090 simplifies implementation.

Q: How much system RAM should I plan for in addition to the GPU?

For offloading 70B models in Q4, 64 GB of DDR5 RAM represent a reasonable minimum. 128 GB leave headroom for the operating system and other parallel processes. RAM bandwidth (high-frequency dual-channel DDR5) directly affects inference speed during offloading: dual-channel DDR5-6000 provides a noticeable gain over DDR4-3200.

Q: Can the RTX 5080 (16 GB) run the models in the catalog?

No. 16 GB of VRAM is insufficient for all the models in the catalog (minimum 71B parameters). Even in Q3_K_M, a 70B model requires approximately 30–32 GB (estimated). The RTX 5080 is suitable for 7–34B models, which are not included in this catalog focused on large LLMs. See our guide to High-end GPU for LLMs for larger-scale multi-GPU configurations.

Q: Does Q4 quantization significantly degrade response quality?

Q4_K_M quantization preserves most of a model’s capabilities compared with FP16 across the majority of text understanding and generation tasks. The most significant degradation occurs on complex mathematical reasoning and precise coding tasks, where Q5_K_M or Q8_0 are preferable if VRAM allows. Q3 and lower quantizations show more visible losses and should be reserved for cases where VRAM is the absolute limiting factor.

Conclusion

In 2026, the best GPU for AI under 2,000 euros is the RTX 4090 for its software maturity, 24 GB of GDDR6X VRAM, and frictionless CUDA ecosystem. If your budget reaches the upper limit and the RTX 5090 is available for under €2,000, the 32 GB justify the extra cost by reducing CPU offloading. The RX 7900 XTX remains the most compelling alternative under €1,050. To find models compatible with your exact configuration, use the BestLLMfor configurator or browse the entire catalog of 249 indexed LLMs.

The hardware for running an LLM locally

To run these models comfortably locally, a RTX 5070 Ti offers an excellent price/performance ratio. Compare prices:

Amazon GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) →

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Article published and updated on by Mohamed Meguedmi · Data source: /api/models.json · Content license: CC BY 4.0.

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Prices in euros (€) are French market prices including VAT, as checked by BestLLMfor. US prices differ: the Amazon buttons show the current US price.