The best GPUs for running large language models locally in 2026

For raw speed, the fastest consumer GPU you can buy for local LLM inference in 2026 is still the NVIDIA RTX 5090 — 32 GB of GDDR7, 1,792 GB/s of bandwidth, and native FP4. The catch is the price. Its $1,999 MSRP is a paper number now: a severe GDDR7 memory shortage has pushed real street prices to roughly $3,000–$3,700 as of mid-2026, with some AIB cards listing north of $4,000. That single fact reshapes this entire guide. At $3,500-plus, the 5090's value lead over a pair of used RTX 3090s, an all-in-one box like NVIDIA's DGX Spark, or an AMD Strix Halo mini-PC is far narrower than it was a year ago.

So this isn't a simple "buy the 5090" list. It's a map of what each tier actually costs right now, and where your money goes furthest depending on the model sizes you care about.

Quick picks:

• Fastest single card: NVIDIA RTX 5090 (if you can stomach the street price)
• Best DIY value / realistic path to 70B–120B: used RTX 3090s in a multi-GPU rig
• Best single-card value: AMD Radeon RX 7900 XTX (used, 24 GB)
• Best budget entry: Intel Arc B580
• Best big-VRAM workstation card: Intel Arc Pro B70 (32 GB) for capacity on a budget; NVIDIA RTX PRO 6000 Blackwell (96 GB) if you have real money
• Best all-in-one "AI in a box": NVIDIA DGX Spark or AMD Ryzen AI Max+ 395 (128 GB unified); Apple Mac Studio if you want the most unified memory of all

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The one fact shaping every price on this list: the memory shortage#

You cannot make a sensible GPU decision in mid-2026 without understanding why prices look the way they do. AI datacenters are absorbing a large and rising share of global memory production — by some projections approaching a majority in 2026, up from a much smaller slice a few years ago. That demand has drained the supply that would otherwise land on consumer graphics cards. The memory alone on a high-VRAM card reportedly costs several times what it did a couple of years ago — enough that it has become the dominant line item in the bill of materials on a card like the RTX 5090. NVIDIA has raised wholesale prices, and everything downstream has followed.

The practical consequences for a buyer:

• Every price here is elevated and volatile. Treat all figures as approximate mid-2026 snapshots, not stable MSRPs. VRAM-heavy cards — exactly the ones you want for LLMs — have been hit hardest.
• Used prices are up too. The classic "find a cheap used 3090/4090/7900 XTX" move still works, but the discounts are shallower than they were.
• Timing matters. If you don't need a card this week, it is reasonable to wait and watch. There's no reliable signal that this eases in the very near term, but paying a memory-crunch premium for a card you won't fully use is the easiest way to overspend right now. Buy for the model sizes you actually run today, not the ones you might run someday.

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What actually matters for local LLM inference#

Before the picks, the one principle that drives all of them: local inference is a memory problem, not a compute problem. The model has to fit in memory first. Once it fits, generation speed is governed mostly by memory bandwidth — how fast weights stream to the compute units. A card with more bandwidth but lower raw TFLOPS will out-generate a "faster" card on nearly every inference workload. And a card with more memory but lower bandwidth (the Intel Arc Pro and unified-memory boxes below) can run bigger models but will feel slower doing it. Capacity decides what you can run; bandwidth decides how fast. Keep that split in mind for everything that follows.

One more wrinkle for 2026: mixture-of-experts (MoE) models change the math. A 120B MoE model only activates a fraction of its parameters per token, so bandwidth-limited hardware (unified-memory boxes especially) runs large MoE models far faster than a dense model of the same nominal size. That's why you'll see a Strix Halo box handle a 120B MoE at usable speeds but crawl on a dense 70B.

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NVIDIA RTX 5090 — Fastest single card, if you can pay for it#

The 5090 is the fastest consumer GPU for local inference, and on raw token generation it isn't close. Its Blackwell architecture brings native FP4 precision — a first for a consumer card — which effectively stretches usable model capacity versus FP8 at comparable quality loss. With 32 GB of GDDR7 and 1,792 GB/s of bandwidth, small models run far faster than you can read, and quantized 30B-class models stay comfortably interactive. In dual-card configurations, community benchmarks have shown real-time speeds on quantized 70B models.

The problem is entirely the price. The $1,999 MSRP is nominal; expect to actually pay somewhere in the $3,000–$3,700 range as of mid-2026, with the worst listings higher still. That doesn't make it a bad card — it makes it a card you should only buy if speed is genuinely your priority and 32 GB covers your models.

Who it's for: Anyone running 7B–34B models daily who wants the fastest possible generation and will hold the card for years. Developers stress-testing quantized 70B models will find it holds up better than any other single consumer option.

Honest trade-offs:

• The street price is roughly double MSRP. At ~$3,500 the value case versus two used 3090s (64–72 GB total), a DGX Spark, or a Strix Halo box is much closer than it looks on the spec sheet.
• 32 GB still won't comfortably run unquantized 70B models — you need aggressive quantization or a second card.
• Runs hot, draws serious power, needs a well-ventilated case.
• If you mostly run 7B models, you are overpaying badly; a used 7900 XTX or 3090 covers that workload for a fraction of the cost.

When to pick something else: If your budget caps under ~$2,000, if you only run sub-13B models, or if your real goal is 70B+ (where a multi-GPU rig or a big-memory box makes more sense per dollar).

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Used RTX 3090 (and multi-GPU rigs) — Best DIY value in 2026#

The single most underrated answer for anyone serious about local inference is not one card — it's two, three, or four used RTX 3090s. Each carries 24 GB of GDDR6X at 936 GB/s, and NVLink/PCIe multi-GPU splits let you stack that memory. A 3–4 card 3090 rig lands in the same price neighborhood as a single DGX Spark (~$4,000) but delivers dramatically higher throughput on large models: community builds report a multi-3090 rig running a 120B MoE model meaningfully faster than a bandwidth-limited box like the DGX Spark — on the order of two to three times on that MoE workload, and a wider gap on dense models, where the rig's aggregate bandwidth really tells. For 70B–120B locally in 2026, this is the standard DIY path, and it's the reason a value-focused list can't ignore it.

The memory crunch has lifted used 3090 prices too, but roughly the cost of a DGX Spark still buys you 72–96 GB of high-bandwidth VRAM here.

Who it's for: Tinkerers who want maximum tokens-per-dollar on big models, are comfortable building a multi-GPU box, and don't mind the power and heat that come with it.

Honest trade-offs:

• Multi-GPU inference means more setup: tensor/pipeline splitting, a motherboard with the lanes, a big PSU, and real cooling. This is a build, not a plug-and-play box.
• Power draw and heat scale with the card count — three or four 350W cards is a space-heater.
• Used GDDR6X cards carry inherent risk; ex-mining units have hard miles. Inspect history where you can.
• No FP4 (Ampere), so you lean on GGUF/FP16/FP8 quantization.

When to pick something else: If you want a quiet, self-contained, low-power box, a DGX Spark or Strix Halo mini-PC trades throughput for convenience. If you only run small models, one 24 GB card is plenty.

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AMD Radeon RX 7900 XTX — Best single-card value#

The 7900 XTX remains AMD's 24 GB value king, and it matters more than ever now that RDNA4's top card, the RX 9070 XT, ships with only 16 GB. It carries 24 GB of GDDR6 at a well-documented 960 GB/s — matching the RTX 4090 on capacity and not far off on bandwidth. RDNA3 is now well supported under recent ROCm 7.x releases (2026), which have reportedly unified the Windows and Linux install path and added native Ollama support for RDNA3/RDNA4 — trimming away the old HSA_OVERRIDE_GFX_VERSION workarounds for supported cards. That was the last real friction point, and it's largely gone.

Pricing is the moving target. Used quotes are contested and rising with the memory crunch: anywhere from roughly $450–$550 up to $750–$850 in mid-2026 depending on source and condition. Even at the high end it's the sharpest single-card value for 24 GB of memory.

If you want a newer architecture and don't need the full 24 GB, the RX 9070 XT (RDNA4, 16 GB) is now first-class under recent ROCm 7.x and worth a look — just accept that 16 GB caps you well below the 7900 XTX on model size.

Who it's for: Budget-conscious enthusiasts running 13B–34B models at good quality without spending four figures. Linux users especially, where ROCm is smoothest.

Honest trade-offs:

• ROCm is better than it's ever been, but it still isn't CUDA. Some frameworks lag on AMD or need manual config.
• Windows support under ROCm has improved with the unified installer but is still secondary to Linux.
• No FP4 — you're on FP16/FP8/GGUF.
• Used pricing is volatile right now; a "$450" deal and an "$850" listing can be the same card two months apart.

When to pick something else: If you're on Windows and won't troubleshoot driver quirks, pay the NVIDIA premium. For 70B+, no single card in this price range does it cleanly.

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NVIDIA RTX 4090 — The proven workhorse#

The 4090 isn't new, but it's still a serious option. With 24 GB of GDDR6X and 1,008 GB/s of bandwidth, it runs 7B–34B models comfortably and has the deepest software support of any card here — every major framework has been tuned against it for years.

The 2026 caveat: the memory shortage has lifted used 4090 prices too, so the old "find a cheap used 4090" framing is weaker than it was. The discount versus a 5090 is real but shallower, and at some listings the gap narrows enough that the newer card's bandwidth and FP4 support make it the better long-term buy.

Who it's for: Anyone who finds a genuinely strong used deal, shops where 5090 supply is thin, or needs rock-solid CUDA compatibility across experimental frameworks.

Honest trade-offs:

• No FP4 (Blackwell-only); FP8/FP16 are the ceiling.
• 1,008 GB/s versus the 5090's 1,792 GB/s is a gap you feel on larger models.
• Used prices are elevated by the shortage — check the 5090 delta before assuming the 4090 is the value play.

When to pick something else: At near-equal pricing, buy the 5090. If you want more total VRAM per dollar, used 3090s in a multi-GPU rig stretch further.

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Intel Arc B580 — Best budget entry#

The B580 is the real story for running small local models without spending real money. At around $249–$299 MSRP (though the memory crunch has pushed street prices above that), it brings 12 GB of GDDR6 at 456 GB/s — generous capacity for the price bracket — and Intel's oneAPI/IPEX-LLM stack has matured enough to run 7B and some 13B models reliably. The B580 launched in December 2024 and has had over a year of driver polish.

Who it's for: Genuinely budget-constrained buyers, or anyone who wants to experiment with local inference before committing more, primarily on 7B models for summarization, coding assist, or chat.

Honest trade-offs:

• 12 GB is a hard ceiling — 34B at any useful quantization won't fit, and some 13B-Q8 models are tight.
• Intel's inference ecosystem is narrower than CUDA or ROCm; expect occasional framework gaps.
• Generation speed trails the AMD and NVIDIA options above on equivalent models.
• A starting point, not a card to grow into.

When to pick something else: If you can stretch to a used 7900 XTX, you double your headroom.

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Intel Arc Pro B70 (and the cheaper B60/B50) — Big VRAM on a budget#

Released in early 2026, the Arc Pro B70 is Intel's big-Battlemage workstation card: 32 GB of GDDR6 at 608 GB/s (BMG-G31 Xe2, 22.94 TFLOPS FP32, 367 TOPS INT8). Street pricing was not firmly established at the time of writing — treat any figure as provisional — but it appears to sit in an interesting spot, roughly a quarter of the 5090's street price for the same 32 GB of VRAM. Bandwidth is much lower, so it's slower, but 32 GB opens up 34B and some quantized 70B models that won't fit on 24 GB cards.

Below it, Intel's "LLM inference ready" workstation tier gives you cheaper on-ramps: the Arc Pro B60 (24 GB GDDR6, ~197 TOPS INT8) around $500 MSRP / $599–$799 street — and notably sold in dual-GPU 48 GB cards — and the Arc Pro B50 (16 GB) at roughly its ~$299 MSRP. For labs that care about VRAM-per-dollar over raw speed, these are worth a serious look.

Who it's for: Workstation and lab builders who need capacity headroom for larger models and can live with slower generation. The dual-GPU 48 GB B60 is a genuinely interesting way to reach big-model capacity cheaply.

Honest trade-offs:

• 608 GB/s (B70) is the limiter — you'll feel it versus a 5090 or PRO 6000.
• Intel's professional inference stack is less mature than NVIDIA's at this tier.
• The capacity only pays off if that's specifically what you need; for speed at 32 GB, a 5090 is faster.

When to pick something else: If speed matters more than model size, pay for the 5090. If you need 70B+ reliably with budget to spare, jump to the PRO 6000 Blackwell.

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NVIDIA RTX PRO 6000 Blackwell — Best single card for 70B+#

This is not a consumer product. The RTX PRO 6000 Blackwell is a professional workstation GPU: 96 GB of GDDR7 (ECC), ~1.8 TB/s of bandwidth, 24,064 CUDA cores, 600W. It's the largest-VRAM discrete GPU you can buy, and it runs 70B models at FP16 — or 120B+ MoE models — on a single card without compromise.

On price, the earlier draft of this guide badly overstated things, so let's be careful. The card launched at around $8,500 in early 2025, and by mid-2026 it is listed roughly $13,000, with street prices spread across major vendors in the ballpark of $11,000–$14,500 — a steep increase over launch, on the order of 50% or so. That's the GPU itself; a matched workstation platform adds more. It is expensive, but it is not the ~$22,000 figure sometimes quoted.

Who it's for: Researchers, labs, and teams running 70B+ models for production inference, fine-tuning, or multi-model workloads on a single card. Not a hobbyist purchase.

Honest trade-offs:

• ~$13,000 is the card, not the system; budget for the rest of the workstation.
• The DIY alternative is real: two RTX 5090s (64 GB) at mid-2026 street prices run roughly $6,000–$8,000+ together — cheaper than a PRO 6000 and enough for many 70B-quantized workloads — with the cost of managing a multi-GPU setup. Three to four used 3090s reach similar capacity for less again, with more assembly.
• ECC and workstation reliability matter in production but are invisible in casual use.
• Overkill for anything below 70B.

When to pick something else: Any scenario where budget is finite and your targets stay under ~34B — a 5090, or a multi-GPU consumer rig, covers it for a fraction of the cost.

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The "AI in a box" tier: DGX Spark, Ryzen AI Max+ 395, and Apple Silicon#

There's a whole category now that sidesteps the GPU question: single boxes with large unified memory shared between CPU and GPU. They trade bandwidth (and therefore speed) for capacity, low power, and simplicity. They're excellent for large MoE models and frustrating for dense 70B decode. Three main paths:

NVIDIA DGX Spark (GB10 Grace Blackwell)#

NVIDIA's flagship 2026 "personal AI supercomputer." It pairs a Grace Blackwell GB10 with 128 GB of coherent unified LPDDR5X and up to ~1 PFLOP of sparse FP4 compute, priced from around $3,999 (Founders), with higher-storage SKUs a few hundred dollars more. It can hold very large models — up to roughly 200B parameters — and runs a 120B model at a reported ~35–50 tok/s for a single interactive user (higher figures you'll see quoted come from batched or concurrent throughput, not single-stream decode).

The hard limit is 273 GB/s of memory bandwidth. That's plenty for MoE models but bottlenecks dense 70B+ decode, and it's why a multi-3090 rig at similar cost can deliver several times the throughput on a dense model — and still a solid margin, roughly two to three times, on a 120B MoE. You're paying for a quiet, compact, low-power, fully supported CUDA box — not for peak speed.

Who it's for: People who want a self-contained NVIDIA/CUDA inference machine with lots of memory and none of the multi-GPU assembly, and who mostly run MoE or mid-size models.

Trade-off in one line: Capacity and convenience, capped by 273 GB/s — if throughput is the goal, a used-3090 rig wins per dollar.

AMD Ryzen AI Max+ 395 (Strix Halo)#

Note the exact name — it's Ryzen AI Max+ 395, with the "+" on "Max." It's an APU: 16 Zen 5 cores, 40 RDNA 3.5 compute units (not RDNA4), an XDNA 2 NPU (~50 TOPS), and up to 128 GB of LPDDR5X-8000 unified memory (about 108 GB usable by the GPU on Linux). Bandwidth is ~256 GB/s theoretical, ~212–215 GB/s in practice. Complete 128 GB mini-PC systems are available under $2,000.

Its strength is MoE throughput — reported around 66–72 tok/s on Qwen3 30B MoE and roughly 31–55 tok/s on a 120B MoE. Dense 70B is where it struggles: single-digit tok/s at Q8, into the low teens at Q4. Correcting an earlier version of this guide: because the iGPU is RDNA 3.5, it does not get RDNA4-specific ROCm features — though RDNA3-class support and Ollama do apply.

Who it's for: Laptop and mini-PC builders (Minisforum, ASUS, and similar) who want big models without a discrete GPU, wall power, or a tower — and who lean on MoE models.

Trade-offs: LPDDR5X bandwidth caps speed; dense 70B is slow; the memory is shared with the system; and you're locked to whatever platform the APU ships in.

Apple Silicon (Mac Studio M3 Ultra / M4 Max)#

The other major unified-memory path. A Mac Studio with M3 Ultra can be configured with up to 512 GB of unified memory — more than anything else here — and Apple's memory bandwidth is materially higher than the LPDDR5X boxes above, which makes it the strongest of the three on dense-model speed at capacity. The MLX and llama.cpp/Metal stacks are mature. The catch is the platform: no CUDA, so some inference frameworks and tooling still assume NVIDIA, and Apple's high-memory configurations are expensive.

Who it's for: People already in the Apple ecosystem who want the most unified memory available and are comfortable outside CUDA.

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Comparison table#

All prices are approximate mid-2026 street figures and are elevated and volatile because of the GDDR7 memory shortage. Verify before buying.

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How to choose#

1. Will the model fit? Start here. Look up your target model size and the quantization you'll accept. A 70B model at Q4 needs roughly 40 GB; a 13B at Q8 needs roughly 13 GB. If the card can't hold it, nothing else matters. For 70B+ you're choosing between one very expensive big-VRAM card, a multi-GPU rig, or a unified-memory box.

2. Bandwidth is your speed dial. Once it fits, tokens-per-second is mostly memory bandwidth. This is why a 5090 beats a 4090 despite "similar" specs, and why the 128 GB unified-memory boxes feel slow on dense 70B despite the huge capacity — 256–273 GB/s can't stream a dense model's weights fast enough.

3. MoE vs. dense changes the answer. If you mainly run large MoE models, the unified-memory boxes (DGX Spark, Strix Halo, Mac Studio) punch well above their bandwidth. If you run dense 70B, prioritize bandwidth — a multi-GPU consumer rig or a PRO 6000.

4. Ecosystem friction is real on AMD and Intel. CUDA has the deepest tooling. Recent ROCm 7.x releases have genuinely closed much of the gap for AMD (a unified Windows/Linux install path, native Ollama), but you'll still hit rough edges, especially with newer experimental frameworks. Intel's oneAPI stack works but is narrower. If you just want to run Ollama and never think about drivers, NVIDIA is still the path of least resistance.

5. Mind the market — and consider waiting. Prices are abnormal right now. If your workload fits a cheaper card, the memory crunch is a reason not to overbuy VRAM you won't use. If you don't need the card this week and your needs aren't urgent, it's reasonable to watch prices before committing.

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The call#

If speed is the priority and 32 GB covers your models, the RTX 5090 is still the fastest single card — just go in knowing you'll likely pay around $3,500, not $1,999. If your real goal is big models on a budget, the honest 2026 answer is a multi-GPU used-3090 rig, which beats every all-in-one box on throughput per dollar. Want big models without building anything? A DGX Spark or a Ryzen AI Max+ 395 mini-PC (or a high-memory Mac Studio) trades speed for a quiet, self-contained box. And if you're just experimenting or running 13B-class models, a used RX 7900 XTX remains the sharpest single-card value — recent ROCm 7.x releases finally make it a clean recommendation.

Whatever you pick: this is an abnormal market. Buy for the models you run today, not the ones you might run later.

This is a starting draft for human editorial review. Prices here are volatile mid-2026 snapshots — re-verify street pricing before publish, as the memory shortage moves fast.