The Best GPUs for Running LLMs Locally in 2026: Ranked and Compared

Elias bought his GPU on the number that gets the marketing: teraflops. He wanted to run a 32B coding model locally, picked a card with a big compute figure and 16 GB of memory, and watched it crawl at two tokens a second. The model did not fit in the card's memory, so it spilled into system RAM, where speed falls off a cliff, five to twenty times slower. The card was fast. The wrong number was on the box.

This guide is the one-stop answer to the question Elias got wrong: which GPU should you actually buy to run large language models on your own hardware. It starts with the beginner basics (the one specification that decides everything, and the quantization math that changed the game), then ranks and compares twelve cards from a $250 entry point to a $30,000 frontier server, with the specific models and precision each one runs best. If you want the deeper concepts first, our guide to choosing a GPU for AI explains the fundamentals; this is the ranked buying decision.

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A model has to load its weights into memory to run, and a GPU can only use a model whose weights fit in its video memory, its VRAM. Exceed that and the model spills into ordinary system RAM, which is far slower to reach. The drop is not gradual: an RTX 5090 running Llama 3.3 70B entirely in VRAM produces well over forty tokens per second, but forced to offload it falls to one or two, slower than you read.

So the buying order is fixed. VRAM capacity decides which models you can run at all. Memory bandwidth decides how fast they run once they fit. Compute, the teraflops on the box, is a distant third for inference. Get the first one right and the rest is tuning.

The reason a 70B model that needed a server two years ago now runs on a workstation is quantization: compressing the weights from 16-bit precision to 8-bit or 4-bit. It cuts memory use by 50 to 75 percent, and the quality cost is smaller than most expect. As a rough guide, FP16 is the full-quality baseline, 8-bit keeps about 99 percent of it, and 4-bit holds 90 to 95 percent, close enough that a 4-bit model is usually hard to tell from the original.

In numbers, a 70B model drops from roughly 140 GB at full precision to about 40 GB at 4-bit. The common formats are GGUF (used by Ollama and llama.cpp, the easiest starting point), and AWQ or GPTQ for GPU-served inference through vLLM. Blackwell cards add native FP4, halving memory again versus 8-bit. The practical effect: quantize sensibly and a model that looked like it needed the tier above often drops into the tier you can afford. Throughout this guide, Q4 (specifically Q4_K_M in GGUF) is the default assumption unless noted.

Here is the mapping that should drive your purchase. These are approximate figures for the model weights at 4-bit, before the extra memory that context length and concurrent users consume, so treat them as a floor and add headroom.

The pattern to hold onto: the 27 to 32B class on a 24 GB card is the value sweet spot of local AI right now. Models like Qwen 32B and DeepSeek-R1 32B deliver strong coding and reasoning and fit a single 24 GB card at 4-bit with context headroom. Below, a 7 to 14B model runs on almost anything with 16 GB. Above, 70B is the wall that forces a 48 GB-plus single card or two 32 GB cards. One caveat the tables skip: the KV cache that holds context grows with prompt length and adds a few gigabytes per concurrent user, so if you serve a team, budget well above the weight-only number.

Twelve cards, ordered roughly from entry to frontier, each with what it runs, the models it suits, and who should buy it. Prices are approximate mid-2026 street figures; a memory shortage has pushed most cards above their official pricing, so confirm live listings and check current GPUs for AI for stock. Here is the whole range at a glance before the detail:

16 GB GDDR7 · ~448 GB/s · Blackwell (FP4) · ~$450

Runs best: 7 to 14B models at Q4 or Q8, comfortably. Think Llama 3.1 8B, Qwen3 14B, Phi-4, Gemma 3, and Mistral 7B for chat, coding help, and simple retrieval, at 50 to 110 tokens per second.

Best for: your first local setup or a lightweight coding assistant. It is the cheapest current card with native FP4 support. The honest caveat is that 16 GB is a one-year decision as models grow, so if you can stretch, the 24 GB used 3090 below buys years of headroom for not much more.

24 GB GDDR6X · 936 GB/s · ~$900 used

Runs best: everything up to the 32B class at Q4. This is the home of Qwen 32B (one of the best dense coding models, around 77 percent on SWE-bench) and DeepSeek-R1 32B for reasoning, plus any 8 to 14B model at higher precision. It delivers roughly 87 percent of a 4090's throughput.

Best for: the serious solo developer who wants the most capability per dollar. Four years after launch it still earns its place, and 24 GB is a genuine three-year decision. If you buy one card and want it to last, this is the smartest money in local AI.

24 GB GDDR6X · 1,008 GB/s · ~$2,400 to $3,500 (discontinued Oct 2024)

Runs best: the same model sizes as the 3090 (up to 32B at Q4, 8B at FP16, 20B without offload) but noticeably faster. Around 120 tokens per second on 8B models fully in VRAM.

Best for: buyers who want maximum 24 GB speed and find one at the right price. The catch is value: it does the same model sizes as a used 3090 for two to three times the money, and the 5090 is faster with more VRAM. Worth it only below about $2,000.

32 GB GDDR7 · 1,792 GB/s · Blackwell (FP4) · ~$3,000 to $5,000

Runs best: a 32B model solo with real headroom (Qwen 32B, DeepSeek-R1 32B), a 34B at Q8, and 45+ tokens per second on a 70B model when it fits. Its quantized inference throughput can exceed an older A100 40 GB on many local workloads.

Best for: the prosumer who wants the fastest single consumer card, or a pair. Two 5090s pool to the 64 GB needed for 70B at Q4. It rarely sells near its nominal price, so shop carefully. The best all-round consumer choice in 2026.

24 GB GDDR6 · 960 GB/s · RDNA 3 · ROCm 7.x · ~$800

Runs best: the same 24 GB model range as a 3090, up to 32B at Q4, on Linux through Ollama and llama.cpp. ROCm 7.x has made this genuinely stable for inference.

Best for: Linux users who want 24 GB for hundreds less than an NVIDIA card. The trade-off is software: NVIDIA remains the default for vLLM, Unsloth, and production tooling, so choose AMD if you mainly use Ollama and value price. See our NVIDIA vs AMD vs Intel breakdown for the full comparison.

12 GB GDDR6 · Battlemage · ~$250

Runs best: 7 to 8B models at Q4, such as Llama 3.2, Phi-4 Mini, and Gemma. Enough to feel real local inference and decide whether to invest further.

Best for: the absolute budget entry and tinkerers. It is the best price-to-performance at the very bottom, but 12 GB caps you at small models, so treat it as a first step rather than a daily driver.

Unified memory up to 512 GB · ~400 to 820 GB/s · from ~$2,000

Runs best: large models that crash a consumer GPU. A 128 GB machine runs Llama 3.3 70B at Q4 around 12 to 15 tokens per second; 256 GB and up can hold quantized 405B-class models. Apple's unified memory means system RAM is the VRAM.

Best for: anyone who wants big-model capacity, silence, and low power without a GPU rig. The trade-offs are lower bandwidth (so slower tokens per second) and life outside the CUDA stack that most training and serving tools target. Unbeatable VRAM-per-watt; not the fastest path.

96 GB GDDR7 ECC · 1,792 GB/s · no NVLink · ~$8,500 (card)

Runs best: a 70B model at FP8 or Q8 on ONE card with real headroom for long context and several users, plus 109B mixture-of-experts models (Llama 4 Scout) at Q4. Around 829 tokens per second on a 32B model in vLLM, and over 1,500 on a 120B model at 4-bit.

Best for: small-team, always-on serving where you want single-card simplicity instead of juggling consumer GPUs. Same bandwidth as a 5090 with three times the VRAM. A complete workstation or server around it runs about $22,000 configured.

80 GB HBM2e · ~2 TB/s · NVLink · data-center

Runs best: a 70B model at Q4 on one card, Llama 4 Scout QLoRA fine-tuning (around 70 to 75 GB with Unsloth), and any 7 to 34B dense model with headroom. The standard workhorse for cost-effective fine-tuning.

Best for: teams that need genuine data-center memory and NVLink scaling for value. It remains the best-value server GPU for mixed inference and fine-tuning, and it is a natural fit to buy and deploy into hosting rather than run on a desk.

80 GB HBM3 · 3.35 TB/s · NVLink/NVSwitch · FP8 · data-center

Runs best: concurrent 70B serving through vLLM, where its real advantage shows: single-stream 70B lands around 25 tokens per second, but dozens of users can share one GPU without proportional slowdown. FP8 accelerates both inference and training.

Best for: production inference and training at scale. This is where serious serving lives, and the point is throughput across many users, not single-stream speed. Bought as servers and deployed into hosting.

141 GB HBM3e · 4.8 TB/s · data-center

Runs best: 70B and larger models at FP16 on a single GPU with room for very large KV caches and long context. Same Hopper compute as the H100 with far more, and faster, memory.

Best for: memory-bound production and long-context serving where the H100's 80 GB is the limiting factor. See our H100 vs H200 vs B200 comparison for the direct trade-off. A buy-and-host card, not a desktop part.

192 GB HBM3e · ~8 TB/s · Blackwell (FP4) · data-center

Runs best: frontier-scale inference and training. It delivers 4 to 5 times the inference throughput of an H100, up to 15 times on optimized LLM workloads, and serves a 70B model at FP16 on one GPU with room to spare for large KV caches. Native FP4 doubles effective throughput again.

Best for: organizations that want maximum throughput per GPU and the lowest system complexity at scale. If the budget allows, it reduces GPU count and cabling. MillionMiner stocks the Blackwell B100 sibling, the same 192 GB board at lower power, shown on this card. The current state of the art, and the top of the AI hardware range.

When one card is not enough, a second gives you additive VRAM, and for inference that works because inference is memory-bound. But a catch buyers miss: NVLink, the fast direct link between GPUs, was removed from consumer cards after the RTX 3090. Two 5090s or two PRO 6000s talk over PCIe instead, capping real scaling at roughly 70 to 80 percent, so two cards give about 1.4 to 1.5 times the throughput, not double.

Two consequences follow. For a model that fits on one card, a single larger card always beats two smaller ones, which is the argument for a 96 GB PRO 6000 over dual 32 GB cards at the 70B tier. And if you do go multi-GPU, standing it up correctly matters; our guide to setting up a multi-GPU server covers the driver, BIOS, and topology steps that decide whether the second card helps at all. Data-center cards with NVLink (the A100, H100, and up) scale far more cleanly at 85 to 93 percent, part of what you pay for at that tier.

Owning is not automatically right. The deciding factor is utilization. A cloud card bills by the hour; an owned card is a fixed cost you amortize across every hour it runs. The crossover is well established: below roughly 70 percent sustained utilization, renting is cheaper over three years, and above about 80 percent, owned hardware breaks even within four to twelve months.

In plain terms: fire up a model a few hours a week and you should rent, and skip the maintenance. Run inference most of the day, every day (an always-on assistant, an internal serving endpoint, continuous fine-tuning) and you cross into buy territory fast. And there the owned path splits again. Running a workstation or a server of GPUs at home means the power draw, heat, noise, and driver upkeep are all yours. The alternative is to own the hardware and place it in a facility built for it: AI GPU hosting, or colocation, gives you the economics of owning with proper power and cooling, so a multi-GPU box runs racked and monitored rather than roaring under a desk. The same power advantage that drove public miners into AI compute is what makes owned-and-hosted GPUs pencil out. Whatever you shortlist, check real throughput on our GPU and AI benchmarks tool first.

Elias returned the fast card with too little memory and bought a used 3090. The 32B model that crawled now runs at usable speed, entirely in VRAM, on a cheaper card. That is the whole guide in one story: the right GPU for local LLMs is the one with enough memory to hold the model you want, then enough bandwidth to run it quickly. Compute is the last thing to worry about.

Decide the model, read the VRAM off the table, buy one tier up for headroom, and check whether your usage justifies owning at all. When it does, the cleanest path is to own the right card or server and host it where power and cooling are handled, and to benchmark the exact card before you commit. Buy for memory, not marketing.

What is the best GPU for running LLMs locally in 2026?
It depends on the model size you want to run, but for most people the value pick is a used RTX 3090 (24 GB) near $900, which handles models up to 32B. The RTX 5090 (32 GB) is the consumer speed king, and the RTX PRO 6000 Blackwell (96 GB) runs 70B on a single card. Match VRAM to your target model first, then optimize for bandwidth.

How much VRAM do I need to run a 70B model locally?
About 40 GB at 4-bit for the weights alone, before context and concurrency, which rules out any single 24 GB card. Realistic options are a 48 GB-plus single card (RTX 6000 Ada, A100), two 32 GB RTX 5090s pooled to 64 GB, a 96 GB RTX PRO 6000, or a 128 GB unified-memory Mac. At full FP16 a 70B model needs roughly 140 GB, which is data-center territory.

What GPU runs Qwen 32B or DeepSeek-R1 32B?
A single 24 GB card handles the 32B class at Q4_K_M, which is why the used RTX 3090 (24 GB) is the value sweet spot. A Qwen 32B needs about 19 GB at Q4 and a DeepSeek-R1 32B about the same, both fitting a 3090, RTX 4090, RTX 5090, or Radeon RX 7900 XTX with context headroom. These 32B models are the best quality you can run comfortably on one affordable card.

Is the RTX 5090 or a used RTX 3090 better for local LLMs?
The 5090 (32 GB) is much faster and holds slightly larger models, but at $3,000 to $5,000 it costs several times a used 3090 (24 GB, around $900). For most users the 3090 is the better value, running the same sizes up to 32B just slower. Choose the 5090 for maximum single-card speed or the extra 8 GB; choose the 3090 for capability per dollar.

Does quantization reduce model quality?
Less than most expect. FP16 is the baseline, 8-bit keeps about 99 percent of quality, and 4-bit holds roughly 90 to 95 percent, hard to distinguish for most tasks while using 50 to 75 percent less memory. Q4_K_M in GGUF is the practical default for local use; reserve 8-bit or FP16 for precision-sensitive work.

Can I run local LLMs on an AMD or Intel GPU?
Yes. AMD's Radeon RX 7900 XTX (24 GB) with ROCm 7.x runs models up to 32B well on Linux through Ollama, and Intel's Arc B580 (12 GB) is a cheap entry for 7 to 8B models. NVIDIA remains the default because CUDA supports vLLM, Unsloth, and production tooling out of the box, but AMD is a legitimate value choice for Ollama-based Linux setups.

Should I buy a GPU or rent cloud GPUs?
It comes down to utilization. Below roughly 70 percent sustained use, renting is cheaper over three years and avoids maintenance; above about 80 percent, owning breaks even within four to twelve months. Rent for occasional or bursty work; buy for always-on inference, internal serving, or continuous fine-tuning. For heavy owned workloads, hosting the hardware captures the ownership economics without running hot cards at home.

Can a Mac run local LLMs instead of a dedicated GPU?
Yes. Apple Silicon uses unified memory shared between CPU and GPU, so a Mac Studio with 128 GB runs a 70B model at 4-bit around 12 to 15 tokens per second, and larger machines hold quantized 405B-class models. The trade-off is lower bandwidth than a dedicated GPU, so slower tokens per second, and being outside the CUDA stack most tools target.

Which GPU is best for a data center or production serving?
For production, the NVIDIA H100 (80 GB) is the standard for concurrent serving via vLLM, the H200 (141 GB) adds memory for larger models and long context, and the B200 (192 GB) delivers 4 to 5 times the H100's inference throughput for frontier scale. The A100 (80 GB) remains the value option. These are bought as servers and typically deployed into hosting where power and cooling are handled.

Sources and image credits
VRAM figures, quantization behavior, model performance, and mid-2026 street prices compiled from independent local-LLM benchmarking and hardware testing, NVIDIA product specifications, and open-model documentation on Hugging Face; prices move with the 2026 memory shortage and should be checked live. GPU photographs are MillionMiner product images and Wikimedia Commons files under CC BY / CC0, with per-card credits shown on each card image, cropped and graded for MillionMiner. The B200-class card shows MillionMiner’s Blackwell B100 board. Diagrams: original MillionMiner graphics. Informational only, not financial advice