Best GPU for local AI in 2026

Answer first

For most people in 2026: a used RTX 3090 24 GB remains the highest-leverage single buy. If you're shopping new, the RTX 4060 Ti 16 GB is the value pick for a 14-32B-class machine, and the RTX 4090 remains the strongest 24 GB “buy it new and don't look back” pick. The RTX 5090 is genuinely better but only if you specifically need 32 GB on one card. On Apple, M4 Max with 64 GB unified memory is the simplest path to 70B-class without a discrete GPU; M3 Ultra Mac Studio with 256 GB or more is the path to 100B+ models.

Below is the long form. If you want the structured engine, hit /choose-my-gpu; if you want a citation-friendly visual of these cards side by side, /hardware/leaderboard.

The 2026 landscape

The cards that are actually in shops or on the used market in 2026 — the only ones worth talking about for a buyer making a decision today.

• RTX 5090 (32 GB, 1,792 GB/s) — the current consumer flagship; often sells above its launch MSRP. Only consumer card above 24 GB.
• RTX 4090 (24 GB, 1,008 GB/s) — sold new and used. The most-recommended single-card pick of the last three years.
• RTX 3090 / 3090 Ti (24 GB, 936 GB/s) — used-only now, well below their launch prices. The price-per-VRAM-GB champion.
• RTX 4060 Ti 16 GB — new. Bandwidth-limited, but one of the cheapest NVIDIA paths to 16 GB.
• M4 Max (64-128 GB unified, 546 GB/s) — laptop or Mac Studio. The premium “quiet 70B machine.”
• M3 Ultra (96-512 GB unified, 819 GB/s) — Mac Studio only. The 100B+ MoE path that doesn't exist on consumer NVIDIA.
• RX 7900 XTX (24 GB, 960 GB/s) — new. The Linux-AMD pick.

Workstation tier (RTX PRO 6000 Blackwell 96 GB, RTX 6000 Ada 48 GB, used RTX A6000 48 GB, NVIDIA L40S 48 GB, H100 80 GB) is its own conversation; for an editorial-language tour see /guides/best-hardware-for-running-local-ai-models. Approximate prices fluctuate week-to-week; the shape of the recommendation doesn't.

By budget

Under $1,000

Two real paths, plus an Apple option. Used RTX 3090 is the highest-leverage pick — 24 GB runs 32B-class models fully on the card (70B in 4-bit needs offload) and the bandwidth (936 GB/s) is genuinely fast. Verify the seller, expect it pulled from a mining rig, plan a 750W+ PSU. RTX 4060 Ti 16 GB new (launch MSRP $499 (2023)) is the safer route — warranty, lower power, fits in any case, but bandwidth-limited (288 GB/s) so 14B-class is the comfortable ceiling. The Apple path is a used or refurbished M2/M3 Mac mini at 16 GB unified, which doubles as a desktop. Full breakdown: /guides/local-ai-hardware-under-1000.

$1,000 - $2,500

The sweet spot of the consumer market. RTX 4090 new (launch MSRP $1,599 (2022)) is the clearest single-card pick — 24 GB at 1,008 GB/s, full warranty, mature drivers, every runtime supports it first. Two used RTX 3090s give 48 GB across two cards via tensor parallelism, but adds the multi-GPU complexity tax (PSU, case, NVLink doesn't pool memory, vLLM-only for some workflows — see /guides/dual-3090-vs-single-5090). M4 Max MacBook Pro 64 GB (previous generation; new and refurbished prices vary) stretches the upper end and shifts the entire stack to MLX + Metal.

$2,500 - $5,000

Now the workstation conversation begins. RTX 5090 (launch MSRP $1,999 (2025)) if you need 32 GB on one card; it often sells above its launch price. Mac Studio M3 Ultra 96 GB is the path to consumer 70B-100B without a multi-GPU rig (previous generation since the M5 Ultra Mac Studio went on sale in September 2026). Used RTX A6000 48 GB is genuinely interesting — 48 GB at workstation tier, mature, for much less than a new workstation card. Triple 3090 is the “72 GB on a budget” option that requires a real homelab plan: see /paths/homelab-operator.

$5,000+

At this tier the question shifts from “which GPU” to “which configuration.” Mac Studio M3 Ultra with 256 GB or more runs Llama 3.3 70B at FP16 and 100B+ MoE comfortably. Quad 3090 / Dual 4090 / Dual 5090 workstation builds are real but operationally heavier — power, heat, noise, multi-GPU runtime config. Production users at this tier should first run /compare/rent-vs-buy-gpu: at this budget, renting for a while can be the cheaper way to find out what you actually need.

By workload

The single best framing question is “what model do I want to run, and what kind of session do I want to run it in?”

• Chat (single-user, daily driver) — RTX 4060 Ti 16 GB or RTX 3090. Single-stream tok/s and TTFT are what you feel; batched serving doesn't matter.
• Coding agent (Continue.dev / Cline / Aider) — RTX 3090 24 GB or RTX 4090 24 GB. Qwen2.5-Coder-32B at Q4 fits comfortably; the agent loop benefits from low TTFT, so prefill bandwidth matters more than chat.
• RAG (long context, document corpora) — 24 GB+ is the floor because KV cache for 32K+ context grows fast. M4 Max 64 GB or M3 Ultra are excellent here because unified memory absorbs context that would OOM a 24 GB discrete GPU.
• Production multi-user serving — vLLM batched serving on a 4090 or 5090, or dual 3090 for 48 GB across two cards. SGLang or TensorRT-LLM if you need the ceiling. Apple Silicon and AMD are not the right fit here in 2026.
• Mobile / on-device — separate question entirely; see the mobile landing at /paths/budget-laptop. The right answer is rarely a discrete GPU.

By software stack

The runtime you plan to use shapes the GPU recommendation as much as the model.

• NVIDIA-only stack (vLLM / SGLang / TensorRT-LLM / ExLlamaV2) — RTX 4090, RTX 5090, RTX 3090, or used A6000. This is the production-serving path; the runtime ecosystem is mature and fast on NVIDIA, narrower elsewhere. SGLang and TensorRT-LLM specifically don't run on AMD or Apple.
• Cross-platform stack (llama.cpp / Ollama / LM Studio) — any GPU works. NVIDIA is fastest but AMD with Vulkan or ROCm runs llama.cpp acceptably, and Apple with Metal runs it natively. For a daily-driver desktop with no production-serving ambition, this is the relaxed path.
• Apple-native stack (MLX / mlx-lm) — M4 Max or M3 Ultra. MLX delivers 70-90% of llama.cpp throughput on equivalent hardware and is genuinely the best-tested path on Apple Silicon. The catch is ecosystem narrowness — image generation, fine-tuning, and serving all lag NVIDIA.
• AMD ROCm stack — RX 7900 XTX or workstation MI-series. On Linux, llama.cpp and vLLM work; on Windows, the story is rougher. See /paths/amd-rocm for the honest state of ROCm in 2026.

Honest tradeoffs

Used 3090 vs new 5090. A used 3090 gets you 24 GB for much less money per gigabyte than a new 5090's 32 GB. The 5090 is roughly 1.8× faster on memory bandwidth and unlocks the 32 GB-only configurations (very long context on 32B models, dual-model setups). For 90% of buyers the 3090 is the right answer; for the 10% who specifically need 32 GB or peak speed, the 5090 has no competitor.

Multi-GPU vs single-flagship. Two 3090s give you 48 GB across two cards at the cost of multi-GPU complexity. NVLink does not pool memory the way a single 48 GB card does — runtimes must be tensor-parallel-aware (vLLM, SGLang, ExLlamaV2 yes; some llama.cpp paths no). If you don't care about a coding agent or production serving and just want to run 70B on one card, a used A6000 48 GB is the simpler answer. The full breakdown: /guides/dual-3090-vs-single-5090.

AMD vs NVIDIA in 2026. The software gap is smaller than it was in 2023 and bigger than the AMD marketing implies. ROCm on Linux is genuinely usable for inference: llama.cpp, vLLM, ExLlamaV2 all work. ROCm on Windows still trails. Image generation tooling (ComfyUI, A1111) is NVIDIA-first. SGLang and TensorRT-LLM don't run on AMD. If you're a Linux operator and you don't need the production-serving stack, the RX 7900 XTX (launch MSRP $999 (2022)) costs much less than a 4090 (launch MSRP $1,599 (2022)). If you need any of the NVIDIA-only runtimes, AMD isn't in the conversation.

The price caveat. Prices shown are launch MSRPs with their year, from our catalog. Used and street prices move every week, and new cards can sell well above launch price when they first ship; Apple configurations vary across refurbished and educational tiers. Check the current price before you buy. Use the chart at /hardware/leaderboard as a relative reference, not a price quote.

Buyer recommendations

The explicit “we'd buy X for Y” calls.

• We'd buy a used RTX 3090 for the operator who wants 24 GB at the lowest sane price and is comfortable with used hardware.
• We'd buy an RTX 4060 Ti 16 GB for the new-PC builder on a budget who wants the warranty and fits in any case. Honest caveat: this is not “the best 70B GPU.” It is “the best budget 14-32B GPU.” 70B at Q4 (about 42 GB of weights) only runs with most of the model offloaded to system RAM, and slowly.
• We'd buy an RTX 4090 for the operator with the budget for one new card who doesn't want to think about it again for three years.
• We'd buy an RTX 5090 for the operator who specifically needs 32 GB on a single card and has the budget for it.
• We'd buy an M4 Max 64 GB MacBook Pro or Mac Studio for the operator who wants quiet, low-power, 70B-capable hardware and isn't doing image generation or production serving.
• We'd buy an M3 Ultra Mac Studio with 256 GB or more for the operator who wants to run 100B+ MoE models without a multi-GPU rig. The economics are unique to Apple Silicon — see /paths/apple-silicon.
• We'd buy a used RTX A6000 48 GB for the operator who wants single-card 48 GB without the new-workstation premium.
• We'd buy two used RTX 3090s for the operator running vLLM with tensor parallelism who wants 48 GB across two cards at the lowest price and accepts the multi-GPU operational tax.

Where the chooser fits in

This guide is the editorial framing — it tells you the shape of the decision. The structured engine that takes your specific budget, workload, OS, and privacy requirements and produces a per-card rationale is at /choose-my-gpu. The benchmark catalog you can check before buying is at /benchmarks; the open queue of GPU+model pairs we'd like measured is at /benchmarks/wanted. The fits-on-this-card check before you click buy is at /will-it-run.