Best GPU for local AI under €1,000 in 2026

Find the best GPU for AI under 1000 euros has become a central question for anyone who wants to run LLMs locally without relying on a cloud subscription. In 2026, the graphics card market offers several credible options in this price range, but choosing without understanding VRAM and quantization constraints is essentially a shot in the dark. The proliferation of open-weight models and the maturity of inference tools such as llama.cpp have made local inference accessible to everyone, provided you choose the right card. This article reviews the GPUs available for under 1,000 €, their real-world performance in tokens per second, software compatibility, and what you can reasonably run with this budget.

VRAM: the decisive criterion for local inference

Before comparing GPUs, you need to understand why video memory (VRAM) is the limiting factor when running LLMs locally.

An LLM loaded into memory occupies space based on the number of parameters and the selected quantization format:

• FP16 (16 bits): ~2 GB per billion parameters
• Q8 (8-bit): ~1 GB per billion parameters
• Q5 (5 bits): ~0.65 GB per billion parameters
• Q4 (4-bit): ~0.5 GB per billion parameters (with a slight measurable loss in quality)

For illustration, the Qwen 2.5 72B Instruct, with its 72 billion parameters, requires approximately 42 GB in Q4 according to the BestLLMfor catalog specs. The Llama 4 Scout 109B, a Meta MoE model with 109B declared parameters, drops to ~65 GB in Q4 thanks to its sparse architecture — which remains beyond a single consumer card, regardless of price range.

GGUF quantization via llama.cpp is now the de facto standard for local inference on NVIDIA GPUs (CUDA) and AMD GPUs (ROCm). It also lets you offload part of the model to system RAM, at the cost of a significant drop in tokens-per-second throughput. To explore the tradeoffs between Q4, Q5, and Q8 formats, see our guide to LLM quantization.

Overview of GPUs under €1,000 in 2026

Here are the graphics cards available within this budget as of today (estimated prices including VAT on the French market, mid-2026):

RTX 4070 Ti Great — 16 GB VRAM - Estimated new price: 680–750 € - Memory bandwidth: 672 GB/s - Architecture: Ada Lovelace, CUDA 12.x - Suitable for: models up to ~13B in Q5/Q8, or ~30B in Q4 with partial offloading

RTX 4080 Super — 16 GB VRAM - Estimated new price: 850–950 € - Memory bandwidth: 736 GB/s - Architecture: Ada Lovelace - Advantage: ~10% additional throughput compared with the 4070 Ti Super under the same workloads

RX 7900 XTX — 24 GB VRAM - Estimated new price: 850–950 € - Memory bandwidth: 960 GB/s - Architecture: RDNA 3, ROCm 6.x - Key advantage: 24 GB lets you load heavier models entirely into VRAM

RTX 3090 (used market) — 24 GB VRAM - Estimated used-market price: 420–580 € - Memory bandwidth: 936 GB/s - Architecture: Ampere, CUDA 11/12 - Value for money: best in this selection for raw LLM inference, with the same VRAM as RX 7900 XTX for often half the price

RTX 3090 Ti (used market) — 24 GB VRAM - Estimated used-market price: 550–700 € - Memory bandwidth: 1,008 GB/s - Note: slightly faster than the 3090, but rare on the resale market

For LLM workloads, the VRAM takes priority over raw TFLOPS. A used RTX 3090 for €500 outperforms a new RTX 4070 Ti Super in practice as soon as models exceed 13B parameters, thanks to its 24 GB versus 16 GB.

Performance in tokens per second: realistic estimates

The figures below are estimates based on community benchmarks published on the Hugging Face Open LLM Leaderboard and the llama.cpp community reports. They vary depending on the llama.cpp version, driver, and system configuration.

• RTX 3090 / 24 GB — 7B model in Q4: estimated 80–120 tokens/sec
• RTX 3090 / 24 GB — 13B model in Q4: estimated 45–70 tokens/sec
• RTX 3090 / 24 GB — 30B model in Q4: estimated at 15–25 tokens/sec (entirely in VRAM)
• RTX 4070 Ti Super / 16 GB — 7B model in Q4: estimated 90–130 tokens/sec
• RTX 4070 Ti Super / 16 GB — 13B model in Q4: estimated 50–75 tokens/sec
• RX 7900 XTX / 24 GB — 13B model in Q4: estimated 50–80 tokens/sec (ROCm may introduce 10–20% variability depending on the version)

As soon as a model exceeds the available VRAM and forces CPU offloading, throughput can drop to 5–15 tokens/sec depending on the amount of system RAM and PCIe bus speed, making productive use difficult.

Software compatibility: CUDA, ROCm, and the inference ecosystem

NVIDIA (CUDA) remains the best-supported platform for local LLM inference:

• llama.cpp CUDA backend: stable, maximum performance, support for all quantization formats
• Front-end ecosystem: LM Studio, Open WebUI, and most mainstream tools prioritize CUDA
• Drivers: mature, extensively documented, regularly updated

AMD (ROCm) has improved considerably since version 6.0:

• llama.cpp ROCm backend: functional, sometimes 10–20% slower than a same-generation NVIDIA GPU on certain advanced formats (IQ3, IQ4), subject to confirmation depending on the versions
• Practical advantage: the RX 7900 XTX offers 24 GB of VRAM at a lower price than a RTX 4080 Super, making it a rational choice despite the less mature ecosystem

What the catalog models imply for infrastructure

One point needs to be stated directly: no model listed in the BestLLMfor catalog (which starts at 71B parameters) fits entirely in VRAM on a single GPU under €1,000. The Qwen 2.5 72B Instruct requires ~42 GB in Q4; the DeepSeek R1 671B requires ~400 GB.

This doesn't mean these models are inaccessible on a modest budget, but it does imply:

• CPU/RAM offload: possible via llama.cpp, but throughput drops to an estimated 5–15 tokens/sec—useful for testing, not production
• Multi-GPU configuration: two used RTX 3090 (~€800–1,000 total) combine for 48 GB of VRAM, allowing you to load the Qwen 2.5 72B Instruct (~42 GB Q4) entirely in VRAM — see our RTX 3090 vs RTX 4090 comparison for configuration details

For projects involving the large models in the catalog, browse our section best LLMs by model family which ranks the options according to infrastructure needs.

Verdict by user profile

Beginner profile / tight budget (budget < €600) Choose a used RTX 3090. 24 GB of VRAM, solid Ampere performance, unbeatable price on the second-hand market. Check the thermal condition (thermal paste, fans) before buying and make sure the card was not used extensively for prolonged mining.

Intermediate profile / new hardware recommended (€600–800) La RTX 4070 Ti Super is the most versatile choice: 16 GB VRAM, recent architecture, better long-term support, moderate power consumption (285 W TDP). Main limitation: heavy models (30B+) are impossible without significant offloading.

Advanced profile / maximize VRAM (€800–€1,000) La RX 7900 XTX (24 GB, ~€900) or the RTX 4080 Super (16 GB, ~€900) depending on your priority: maximum VRAM vs. raw throughput and software compatibility. For LLMs, the RX 7900 XTX wins thanks to its 24 GB, despite the ROCm ecosystem being less established than CUDA.

FAQ

Q: Is 16 GB of GPU memory enough to run LLMs locally in 2026?

16 GB lets you run models up to ~30B in Q4 with a little CPU offload, or up to ~13B in Q5/Q8 without offload and at a comfortable throughput. For models in the BestLLMfor catalog (71B+), 16 GB isn't enough without a multi-GPU setup. It's a good starting point for exploring local inference, but a real limitation as soon as your needs grow.

Q: Is it better to buy new or used for LLM use?

For LLM inference, used Ampere GPUs (RTX 3090, 3090 Ti) offer hard-to-beat value thanks to their 24 GB of VRAM. The main risk is mechanical wear from intensive mining use. Buy from sellers with a track record, test stability under load with a GPU stress-testing tool, and check whether any remaining manufacturer warranty applies.

Q: Is the RX 7900 XTX actually usable for LLMs with ROCm?

Yes. Since ROCm 6.x and recent versions of llama.cpp, the RX 7900 XTX is fully functional for local inference. Performance is slightly lower than that of a same-generation NVIDIA GPU on some advanced quantization formats (to be confirmed depending on the versions), but the advantage of 24 GB of VRAM more than makes up for it for models exceeding 13B parameters.

Q: How much VRAM do you need to run a 70B model?

A model with ~72B parameters requires about 42 GB in Q4 — this is the value listed for the Qwen 2.5 72B Instruct in the BestLLMfor catalog. In Q5, expect about 50 GB, and in FP16, about 144 GB. This requires at least two 24 GB consumer GPUs, or a professional GPU beyond the budget.

Q: Can you do LoRA fine-tuning with a GPU under €1,000?

PEFT/LoRA fine-tuning, described in the seminal work on arXiv:2106.09685, is accessible with 16–24 GB of VRAM for models with 7B to 13B parameters. Full fine-tuning of a 7B model in FP16 requires approximately 30–40 GB with gradients, exceeding the capacity of a single GPU under €1,000. For larger models in the lineup, local fine-tuning remains out of reach for a consumer single-GPU setup.

Q: What is the practical difference between inference entirely in VRAM and inference with CPU offloading?

With a model entirely in VRAM, throughput is limited by the GPU's memory bandwidth (e.g., 936 GB/s on RTX 3090), typically 20–120 tokens/sec depending on model size. With CPU offload, throughput is constrained by the PCIe bus (~32 GB/s in practice on PCIe 4.0 x16), which can reduce throughput by 10 to 30 times. Offload is useful for experimentation; it is not suitable for continuous production use.

Conclusion

Choose the best GPU for AI under 1000 euros in 2026 comes down to a clear trade-off: 16 GB of VRAM for mainstream models with a new card and warranty, or 24 GB used with a RTX 3090 for hard-to-beat value. The catalog's large open-weight models require more infrastructure, but the more compact models remain accessible and capable at this budget. Use our configurator to identify LLMs suited to your GPU, or explore the BestLLMfor catalog with its 249 models filterable by required VRAM and license.