Best GPU for Local LLMs in 2026: 5 Picks for Home Inference

Local LLM inference has one real bottleneck: VRAM. If the model fits in your GPU’s memory, you get 30–200+ tokens per second depending on the card. If it doesn’t fit, layers offload to system RAM over PCIe and generation speed drops to 2–5 tokens per second — barely usable for interactive chat.

In March 2026, the GPU market for home AI looks different than a year ago. The RTX 5090 shipped with 32 GB of GDDR7, finally breaking the 24 GB ceiling on consumer cards. RTX 4090 production ended in late 2024, making new units scarce and overpriced. Used RTX 3090s continue to be the value play. And AMD’s ROCm stack has improved enough that the RX 7900 XTX is at least a conversation, though CUDA still holds a commanding software lead.

This guide ranks five GPUs for running Ollama, llama.cpp, vLLM, and other local inference engines at home. Every benchmark number below was sourced from real-world testing — not synthetic benchmarks or manufacturer claims.

Our Pick: NVIDIA RTX 3090 (Used) — Best Value for 24 GB VRAM

The used RTX 3090 is the GPU I’d recommend to most home lab builders running local LLMs in 2026. The math is straightforward: 24 GB VRAM at $800–900 used versus $2,000+ for a new RTX 4090 with identical VRAM capacity.

Specs: 24 GB GDDR6X · 384-bit bus · 936 GB/s bandwidth · 10,496 CUDA cores · 350W TDP

LLM Benchmarks:

• Llama 3 8B Q4_K_M: ~112 tok/s generation
• Llama 2 13B Q4: ~85 tok/s generation
• 32B models (Q4): Fits in VRAM, ~20–25 tok/s
• 70B models: Does not fit — requires offloading (unusable speed)

The RTX 3090 has the same 24 GB of GDDR6X as the RTX 4090, which means it runs the exact same model sizes. A 13B model at Q4 quantization? Fits. A 32B model at Q4? Fits with room for KV cache. The only thing 24 GB can’t do is run a 70B model on a single card.

The performance gap versus the 4090 is real but modest. On Llama 3 8B, the 3090 generates ~112 tok/s versus the 4090’s ~128 tok/s — about 15% slower. On 13B models, the gap narrows further because both cards hit similar memory bandwidth walls. For a single-user setup where anything above 30 tok/s feels “instant,” that 15% difference is imperceptible.

At $800–900 on the used market, the 3090 costs roughly half what a 4090 sells for. That’s the same VRAM capacity, 93% of the memory bandwidth (936 vs. 1,008 GB/s), and 85% of the inference speed. No other GPU in the consumer market delivers this ratio of performance to price for LLM workloads.

The 350W TDP is worth planning for. At $0.15/kWh running 24/7 under moderate inference load (~200W actual draw), that’s roughly $260/year in electricity. Budget for a 750W+ UPS and verify your PSU has the headroom.

Buying used: The RTX 3090 launched during the crypto mining boom, so many used units are ex-mining cards. This sounds worse than it is — GPUs don’t wear out from compute workloads the way mechanical drives do. The main risk is degraded fans. Check VRAM junction temperature with GPU-Z (should be under 100°C under load), verify there are no visual artifacts in stress tests, and buy from sellers who offer at least a 30-day return window.

For a deeper comparison, see our RTX 3090 vs 4090 for local LLMs guide.

Best Performance: NVIDIA RTX 4090

The RTX 4090 is the fastest 24 GB GPU you can buy. If you want maximum inference speed and money isn’t the constraint, this is it.

Specs: 24 GB GDDR6X · 384-bit bus · 1,008 GB/s bandwidth · 16,384 CUDA cores · 450W TDP

LLM Benchmarks:

• Llama 3 8B Q4_K_M: ~128 tok/s generation, ~7,000–9,100 tok/s prompt processing
• Llama 2 13B Q4: ~110 tok/s generation
• 32B models (Q4): Fits in VRAM at ~18–20 GB, ~25–30 tok/s
• 70B models: Does not fit on a single card

The Ada Lovelace architecture brings 4th-gen Tensor Cores and 60% more CUDA cores than the 3090, which translates into meaningfully faster prompt processing (important for long context windows) and ~15–20% faster token generation. If you’re running an inference server that handles multiple concurrent requests — say, a shared Open WebUI instance for your household or a coding assistant that multiple dev environments hit — the 4090’s extra throughput matters.

One underappreciated advantage: power efficiency during inference. The 4090’s 450W TDP rating is for peak gaming load. During LLM inference, actual power draw sits around 235W — significantly more efficient per token than the 3090.

The problem is availability. NVIDIA ceased RTX 4090 production in October 2024 to make way for the 5090. New units on Amazon are scarce and priced at $2,000–3,500, well above the original $1,599 MSRP. Used units run $1,800–2,200. At those prices, the cost-per-token advantage over a used 3090 evaporates.

Buy the 4090 if you find one at or near $2,000 and you want the fastest 24 GB card available. But for most home lab users, the used 3090 at half the price running the same model sizes is the more rational choice.

Most Powerful: NVIDIA RTX 5090

The RTX 5090 is the first consumer GPU that can run a 70B parameter model at Q4 quantization on a single card. That alone makes it a category-defining product for local AI.

Specs: 32 GB GDDR7 · 512-bit bus · 1,792 GB/s bandwidth · 21,760 CUDA cores · 575W TDP

LLM Benchmarks:

• Llama 3 8B Q4_K_M: ~213 tok/s generation, ~10,400 tok/s prompt processing
• 32B models (Q4): ~61 tok/s generation
• 70B models (Q4): Fits on a single card — ~27 tok/s (2-card NVLink matches an H100)
• 120B models: Feasible with aggressive quantization and partial offload

The raw numbers are staggering. 1,792 GB/s memory bandwidth is 77% more than the 4090 and nearly double the 3090. On Llama 3 8B, the 5090 generates 213 tok/s — so fast that the bottleneck shifts from the GPU to the speed at which you can read the output. On 32B models, 61 tok/s is faster than what the 4090 delivers on 8B models. This is a genuine generational leap.

The 32 GB GDDR7 is the real story for LLM users. The jump from 24 GB to 32 GB crosses a critical threshold: 70B parameter models at Q4 quantization require roughly 28–30 GB of VRAM. The RTX 5090 fits them. On 24 GB cards, 70B models require offloading layers to system RAM, which kills interactive speed. The 5090 eliminates that limitation entirely.

The trade-offs are severe. The 575W TDP is not a typo — under AI workloads, measured power draw hits 560–590W with transient spikes above 900W. You need a high-quality 1000W+ power supply, robust case cooling, and a UPS sized accordingly. The card is physically enormous at 3.5+ slots. And the street price remains well above the $1,999 MSRP — expect to pay $2,800–4,200 for available units due to ongoing demand and supply constraints.

Buy the 5090 if you specifically need 32 GB VRAM for 70B+ models or require maximum throughput for a multi-user inference server. For 13B and 32B model workloads — where most home lab users operate — a 24 GB card at half the price does the job.

Best AMD Option: Radeon RX 7900 XTX

The RX 7900 XTX is AMD’s answer for local LLM inference — 24 GB VRAM with 960 GB/s bandwidth at a lower price than NVIDIA’s 24 GB options. The hardware is competitive. The software story is more complicated.

Specs: 24 GB GDDR6 · 384-bit bus · 960 GB/s bandwidth · 6,144 stream processors · 355W TDP

LLM Benchmarks (llama.cpp with ROCm):

• Llama 3 8B Q4: ~37 tok/s generation
• 13B–14B models: ~32 tok/s generation
• 32B models (Q4): Fits in 24 GB VRAM, ~15–18 tok/s
• 70B models: Does not fit — same 24 GB limitation as NVIDIA

Read those benchmark numbers carefully. On paper, the 7900 XTX has 960 GB/s bandwidth — within 5% of the RTX 4090’s 1,008 GB/s. In practice, llama.cpp on ROCm generates 37 tok/s on Llama 3 8B versus the 4090’s 128 tok/s with CUDA. That’s a 3.5x gap driven almost entirely by software optimization, not hardware capability.

AMD has made real progress. ROCm now officially supports llama.cpp, Ollama runs via the ROCm backend, and PyTorch 2.5+ added Flash Attention for RDNA 3 (gfx1100). vLLM added official AMD support in early 2026. On specific optimized benchmarks — AMD demonstrated the 7900 XTX matching or slightly exceeding the 4090 on DeepSeek 7B and 8B models in controlled tests. But in the general case, across a variety of models and inference frameworks, the CUDA stack remains significantly faster.

The practical reality in March 2026: if you buy a 7900 XTX, plan on running Linux. ROCm on Windows is still immature. Expect to spend extra time on driver configuration, framework compatibility, and debugging issues that NVIDIA users never encounter. The community support on r/LocalLLaMA skews heavily NVIDIA — when something breaks, you’ll find fewer people who’ve solved the same problem on AMD.

Who should buy it: If you find a 7900 XTX for under $850 used (they’re available around $825 on eBay) and you’re comfortable with Linux and ROCm, you get 24 GB VRAM for less than a used RTX 3090. That’s a legitimate value proposition. New units at $1,334 on Amazon are harder to justify when a used 3090 offers better inference performance for $500 less.

For the full breakdown, see NVIDIA vs AMD for local LLMs.

Budget Pick: NVIDIA RTX 4060 Ti 16GB

The RTX 4060 Ti 16GB is the cheapest NVIDIA GPU that can run 13B models entirely in VRAM. At $450–550 new with full warranty, it’s the entry point for home lab LLM inference.

Specs: 16 GB GDDR6 · 128-bit bus · 288 GB/s bandwidth · 4,352 CUDA cores · 165W TDP

LLM Benchmarks:

• Llama 3 8B Q4_K_M: ~89 tok/s generation
• Llama 2 13B Q4: ~14 tok/s generation
• 32B+ models: Does not fit — requires heavy offloading

The 16 GB VRAM is the reason this card exists for LLM users. The standard 8 GB RTX 4060 Ti can barely fit a 7B model with minimal context window. The 16 GB variant fits 13B models at Q4 quantization, which opens up meaningfully more capable models — Llama 3 13B, Mistral 7B×2 (Mixtral 8x7B with some offloading), and various fine-tuned 13B chat models.

The 128-bit memory bus is the hard limitation. At 288 GB/s, the 4060 Ti 16GB has roughly one-quarter the memory bandwidth of the 3090 or 4090. On 7B–8B models, the CUDA cores compensate and you still get 89 tok/s — perfectly usable. But on 13B models, the bandwidth bottleneck hits hard: ~14 tok/s is functional for a personal coding assistant but sluggish for interactive chat.

Where the 4060 Ti 16GB genuinely excels is power efficiency. At 165W TDP (and ~100W actual draw during inference), this card costs roughly $130/year to run 24/7 at $0.15/kWh. Compare that to $260/year for a 3090 or $310/year for a 4090. If you’re building a dedicated always-on inference server for 7B–8B models — say, a local coding assistant running Ollama — the 4060 Ti 16GB makes more economic sense over a 2-year period than a power-hungry 24 GB card you don’t fully utilize.

The honest recommendation: if LLMs are your primary use case and you can stretch the budget to $800–900 for a used 3090, do that instead. The jump from 16 GB to 24 GB VRAM and from 288 GB/s to 936 GB/s bandwidth is transformational. The 4060 Ti is the right choice only if your budget is truly capped under $600 or power consumption is a hard constraint.

How to Choose: Buying Criteria

VRAM Is the Gating Factor

For LLM inference, VRAM determines the maximum model size you can run at interactive speed. Here’s the real-world capacity at Q4_K_M quantization (the most common setting in Ollama):

Once a model exceeds your VRAM, layers offload to system RAM. PCIe 4.0 x16 delivers ~25 GB/s versus 936+ GB/s for GPU memory bandwidth. That’s a 37x slowdown on the offloaded layers. Even offloading 10% of layers to RAM cuts generation speed in half.

The practical takeaway: buy enough VRAM to fit the model size you actually want to run. Don’t plan on “offloading a few layers” — the performance cliff is steep.

Memory Bandwidth Determines Speed

Once the model fits in VRAM, memory bandwidth determines how fast tokens generate. LLM inference is memory-bandwidth-bound, not compute-bound. This is why:

• RTX 5090 (1,792 GB/s): 213 tok/s on 8B
• RTX 4090 (1,008 GB/s): 128 tok/s on 8B
• RTX 3090 (936 GB/s): 112 tok/s on 8B
• RTX 4060 Ti (288 GB/s): 89 tok/s on 8B

The 4060 Ti’s 89 tok/s on 8B despite having 3.5x less bandwidth than the 3090 shows that CUDA core count helps on smaller models. But on 13B models where more memory gets accessed per token, the 4060 Ti drops to 14 tok/s while the 3090 stays at 85 tok/s. Bandwidth dominates at scale.

Power and Cooling Are Real Constraints

These cards run in your home, not a data center. Plan accordingly:

The RTX 5090’s transient power spikes can exceed 900W momentarily. A cheap PSU will trip its overcurrent protection. Use a high-quality unit from Corsair, Seasonic, or be quiet! rated at 1000W+. Your UPS needs to handle the spike, not just the sustained draw.

For always-on inference servers, the 4060 Ti’s $130/year operating cost versus the 3090’s $260/year is a real consideration over a 3-year lifespan.

New vs. Used: The Real Calculation

The used GPU market favors LLM builders in 2026:

• RTX 3090 used ($800–900): Best overall value. Same VRAM as the 4090, 85% of the speed, half the price.
• RTX 4090 used ($1,800–2,200): Only worthwhile if you need maximum 24 GB speed and can’t justify the 5090.
• RX 7900 XTX used ($825): Compelling on paper, but ROCm software overhead erases the price advantage versus a similarly-priced 3090.

New cards make sense for the RTX 4060 Ti 16GB (warranty matters at the budget tier) and the RTX 5090 (no used market yet). For 24 GB NVIDIA cards, buying used is the rational move.

NVIDIA vs. AMD for LLMs

This isn’t close yet. CUDA’s ecosystem advantage translates into 2–3.5x faster inference on equivalent hardware. The RTX 3090 at $800–900 used outperforms the RX 7900 XTX at $825 in every LLM benchmark despite having slightly less memory bandwidth on paper. Ollama, llama.cpp, vLLM, and PyTorch are all optimized for CUDA first.

AMD is viable if: (1) you find a 7900 XTX significantly cheaper than a used 3090, (2) you run Linux, and (3) you’re willing to troubleshoot ROCm issues that NVIDIA users never see. The gap is narrowing — but in March 2026, NVIDIA remains the default recommendation.

Bottom Line

For most home lab builders running local LLMs, the used RTX 3090 at $800–900 is the right GPU. It delivers 24 GB VRAM — the sweet spot for models up to 32B parameters — with 936 GB/s bandwidth that keeps inference fast. No other GPU matches its performance-per-dollar for this workload.

If you want maximum speed on a 24 GB card and can stomach the price, the RTX 4090 at $2,000+ is 15–20% faster. If you need 70B models on a single card, the RTX 5090 at $2,800+ is the only consumer option.

On a tight budget, the RTX 4060 Ti 16GB at $450–550 gets you into local AI with enough VRAM for 7B–8B models at excellent speed and the lowest power draw in the group.

The RX 7900 XTX is a credible option at under $850 used for Linux users comfortable with ROCm, but CUDA’s software lead makes NVIDIA the safer bet for most people.

Whatever you choose, pair it with a machine that can handle the power draw. See our home lab starter guide for full build recommendations, and check how much VRAM you need for LLMs to match your GPU to the models you want to run.