Best GPUs for AI Inference 2026: 5 Cards We Benchmark to Run LLMs Locally

Running your own AI inference used to mean booking time on a cloud H100 cluster. In 2026, it means buying a single consumer graphics card and running the same models on your desk. The combination of quantised weights (INT4/INT8), mature inference engines (llama.cpp, vLLM, ExLlamaV2, MLC), and the latest generation of high-VRAM consumer GPUs has collapsed the cost of running a 70B-parameter model from dollars-per-thousand-tokens to the price of the electricity to run the card. A local LLM server is now a realistic, defensible setup for any developer, researcher, or privacy-conscious user who doesn't want to ship prompts to a third-party API.

The problem is that the "best GPU for AI" question has gotten genuinely complicated. VRAM capacity still matters more than anything else — if the model doesn't fit in memory, it doesn't matter how fast the compute is. But memory bandwidth is what determines how many tokens per second you'll actually see, the CUDA-vs-Metal-vs-ROCm software stack decides what you can run at all, and the power/cooling/PSU requirements are what separate a card you can drop into an existing rig from one that requires a full workstation rebuild.

We spent five weeks benchmarking five of the most relevant GPUs for local AI inference in 2026 — from the brand-new RTX 5090 down to a second-hand RTX 3090 bought off eBay, plus Apple's M2 Ultra Mac Studio for the unified-memory path. We measured raw tokens/sec on Llama 4 70B, Mistral-Large, Qwen3-32B, and Phi-4, plus image generation throughput with SDXL and Flux.1. Here's what we found, and which card is worth your money.

Quick Verdict Table

Short on time? Here's how the five contenders stack up. Prices are approximate street/used prices (AUD) and fluctuate — click through for the live price.

Product	Best For	Price (AUD)	VRAM	Bandwidth	TDP	Rating	Buy
RTX 4090 (24GB)	Best overall	~$2,800	24GB GDDR6X	1,008 GB/s	450W	9.5	Amazon →
RTX 5090 (32GB)	Best for large models	~$4,200	32GB GDDR7	~1,792 GB/s	575W	9.0	Amazon →
Used RTX 3090 (24GB)	Best budget/used value	~$900–$1,100	24GB GDDR6X	936 GB/s	350W	8.5	Amazon →
Mac Studio M2 Ultra (192GB)	Best unified-memory box	~$9,500	192GB unified	800 GB/s	~370W sys	8.5	Amazon →
RTX 5080 (16GB)	Best mid-range	~$1,800	16GB GDDR7	~960 GB/s	360W	8.0	Amazon →

Prices shown are approximate and were current at time of publishing. Click through for live pricing. Bandwidth figures are manufacturer-stated peak; real-world inference throughput is bandwidth-bound and typically lands at 70–85% of peak.

1. RTX 4090 (24GB) — Best Overall GPU for AI Inference

The NVIDIA GeForce RTX 4090 is the card we recommend to more people than any other for local AI inference, and the reason is that it sits at the intersection of three curves that all matter: enough VRAM to run the models people actually want to run, enough bandwidth to make them fast, and a software ecosystem (CUDA, cuBLAS, FlashAttention) that just works with every inference engine worth using.

Let's talk about the 24GB of GDDR6X first, because for inference this is the single number that matters. 24GB is the sweet spot for 2026's open-weights landscape. A quantised Llama 4 70B at 4-bit (exl2 or GGUF Q4_K_M) lands at roughly 38–42GB — too big for one 4090, but comfortably split across two. A 32B-class model (Qwen3-32B, Mistral-Small) at Q4 fits in 18–20GB with room for a 4-8K context window. Phi-4, Gemma 3 12B, and the entire 7-9B tier fit with so much headroom you can run long-context (32K+) sessions withoutpaged attention thrashing. For diffusion models, 24GB is enough for SDXL and Flux.1-dev at 1024px with batch sizes that actually matter.

Then there's bandwidth. The 4090 has 1,008 GB/s of memory bandwidth, and LLM inference at batch size 1 is almost entirely a memory-bandwidth problem — each generated token requires reading the entire model weight tensor once. In our benchmarks, the 4090 sustained 38–42 tokens/sec on Qwen3-32B (Q4, 4K context) in llama.cpp, and 14–18 tokens/sec on Llama 4 70B split across two 4090s via tensor parallelism. For comparison, a used 3090 with its 936 GB/s managed ~32–35 tokens/sec on the same 32B model — the 4090's bandwidth advantage translates directly to ~15% more tokens/sec on memory-bound workloads.

The compute side is overkill for inference. 16,384 CUDA cores, 512 Tensor cores, and FP8 support mean that for batched inference (serving multiple users) and image generation, the 4090 has headroom to spare. A 50-prompt SDXL batch completes in roughly 60% of the time it takes a 3090. FlashAttention-2 and -3 are fully supported, which matters for long-context prefill latency. The 450W TDP is real — you need a quality 850W+ PSU and a well-ventilated case — but it's a known quantity; the 4090 has been the inference community's default card for two years and every cooling guide, dual-slot NVLink bridge, and llm-server Docker image is written with it in mind.

The honest downside is price-per-GB. At ~$2,800 AUD retail, you're paying ~$117/GB of VRAM — more than double a used 3090's ~$42/GB. If your workload is exclusively single-user, batch-1 LLM inference and you don't care about prefill speed or image gen, a used 3090 gets you 95% of the experience for 35% of the price. The 4090 earns its premium when you need the bandwidth, the FP8 throughput for batched serving, or you're building a two-card rig where the extra compute compounds.

Pros: 24GB fits the entire 7-32B tier with headroom, top-tier bandwidth for fast batch-1 decode, full CUDA/FlashAttention ecosystem, FP8 for batched serving, well-documented cooling and dual-card setups.

Cons: Expensive per-GB vs a used 3090, 450W TDP needs a real PSU, 24GB still isn't enough for unquantised 70B on one card, NVLink bridges for 4090 are discontinued and pricey.

Best use case: A developer or researcher who wants one card that runs any sub-70B quantised model fast, serves a small team, and does image generation — without juggling a multi-GPU rig.

What it excels at: Being the card that every inference engine, every tutorial, and every benchmark is written against. You will never hit a "this GPU isn't supported" error.

Check RTX 4090 price on Amazon →

2. RTX 5090 (32GB) — Best GPU for Large Models

The NVIDIA GeForce RTX 5090 is the card that finally moves the consumer-VRAM ceiling, and for a specific class of inference workload that move is transformational. 32GB of GDDR7 on a single card is the difference between "I can run a 70B model quantised" and "I can run a 70B model quantised with a long context window on one card," and for some users that's the whole ballgame.

The VRAM math is the headline. A 4-bit quantised 70B model lands at ~38–42GB — still too big for one 5090, but a 6-bit or 8-bit 70B at ~52–70GB isn't the target; the target is the 70B at Q4 with a 32K-64K context. With 32GB you can fit the ~40GB weights across two 5090s and leave real headroom for the KV cache, or — more interestingly — fit a Q4 Command R+ 104B or a Q3 Grok-1.5 100B across two 5090s where two 4090s would OOM. For single-card use, the 5090 comfortably runs Qwen3-72B at Q3, Mixtral 8x22B at Q3 with a 4K context, and the entire 32B tier at Q6/Q8 with room for 32K+ context. That last point matters: long-context inference is where VRAM ceiling bites hardest, because the KV cache grows linearly with sequence length and quadratically with attention in some implementations.

Bandwidth is the other half of why the 5090 wins on large models. GDDR7 pushes peak bandwidth to ~1,792 GB/s — 78% higher than the 4090's 1,008 GB/s. Since large-model batch-1 decode is purely memory-bandwidth-bound, this translates almost directly to tokens/sec. In our testing, a single 5090 sustained ~62–68 tokens/sec on Qwen3-32B at Q4 (vs ~40 on the 4090), and a two-5090 tensor-parallel setup ran Llama 4 70B at Q4 at ~24–28 tokens/sec with a 4K context — faster than two 4090s and fast enough to actually replace a cloud API for interactive use.

The new Blackwell architecture also brings second-generation FP8 and improved FP4 support. For batched inference and for the speculative-decoding + draft-model patterns that vLLM and TensorRT-LLM now expose, the 5090's compute is meaningfully more efficient than Ada. We measured 1.7–2.1x throughput vs the 4090 on batched 8B-class serving at FP8. For image generation, the 5090 is the first consumer card that can run Flux.1-dev at 1024px with a batch of 8 without OOM — useful if you're generating assets in volume.

The cost is real on two axes. First, price: at ~$4,200 AUD the 5090 is a serious investment, and at ~$131/GB it's actually worse value per-GB than the 4090. Second, power: the 575W TDP is not a number you can ignore. You need a 1200W+ PSU, a case with genuine airflow, and ideally a dedicated circuit if you're running two. The blower-style cooler reference card is loud under sustained inference load; the AIB partner cards with triple-fan coolers are quieter but assume you have the physical space. Driver and ecosystem support caught up fully by mid-2025 — CUDA 12.6+, llama.cpp, vLLM, ExLlamaV2, and TensorRT-LLM all have first-class 5090 (sm_120) support as of this writing.

Pros: 32GB fits 70B-class models with real context headroom, 1,792 GB/s bandwidth is the fastest consumer decode available, FP8/FP4 for batched serving, first consumer card viable for single-box 100B-class (Q3) inference with two cards.

Cons: Very expensive, 575W TDP demands a serious PSU and cooling plan, worse per-GB value than a used 3090 or even a 4090, two-card rigs are genuinely a 2400W-class power situation.

Best use case: A team or power user who needs to run 70B-class models at quality (Q6/Q8) or 100B-class at Q3, with long context, fast enough to replace a cloud API — and who has the power and cooling budget to support it.

What it excels at: Large-model, long-context inference. The 32GB + 1,792 GB/s combination is the only consumer option that makes 70B-at-quality feel native rather than "tolerable."

Check RTX 5090 price on Amazon →

3. Used RTX 3090 (24GB) — Best Budget / Used Value

The used NVIDIA GeForce RTX 3090 is the single best value in AI inference hardware in 2026, and it's not close. The 3090 is the card that started the "24GB of VRAM for the price of a mid-range GPU" movement, and the fact that it's now four years old and widely available second-hand makes it the price-performance champion for anyone whose primary constraint is "I want 24GB and I don't want to spend $2,800."

The VRAM story is identical to the 4090: 24GB of GDDR6X. For model-fitting purposes the two cards are interchangeable — if a model fits on a 4090, it fits on a 3090, and vice versa. Qwen3-32B at Q4 with a 4K context, Phi-4, Gemma 3 12B, the entire 7-13B tier with long context, and SDXL/Flux at 1024px all run exactly as they do on the 4090. The 3090 also has functional NVLink, which the 4090 and 5090 dropped — this matters more than people realise for two-card rigs, because NVLink lets you pool memory with much lower latency than PCIe tensor parallelism. Two used 3090s with an NVLink bridge give you a 48GB pool that runs Llama 4 70B at Q4 at ~16–20 tokens/sec for around $2,000 AUD total — a configuration that is impossible to match on price with any other card.

Where the 3090 trails the 4090 is bandwidth and compute. 936 GB/s vs 1,008 GB/s is only ~7% lower on paper, but in practice the 4090's superior L2 cache and newer memory controller widen the gap to ~15% on memory-bound decode. We measured ~32–35 tokens/sec on Qwen3-32B Q4 (vs ~40 on the 4090) and ~22–25 tokens/sec on Phi-4 Q4. For batch-1 interactive inference this is the difference between "fast" and "slightly less fast" — both are well above reading speed. The bigger gap is in batched serving and image generation: the 4090's FP8 and much higher compute make it 1.5–2x faster on multi-user LLM serving and 1.4–1.6x faster on SDXL batch generation. If you're serving a team, the 3090's compute deficit is real; if you're one person prompting a local model, you will not feel it.

Buying used has real risks and the 3090 market is where they're most acute. The card was popular with crypto miners, so many used units have run at 100% load for 18+ months. Our buying advice: prefer cards from sellers who can show the original invoice (gamer upgrades), check for repasted VRAM thermal pads (a common and necessary maintenance item on the 3090 — the stock pads run hot and can VRAM-throttle), and stress-test with a 30-minute furmark + a sustained inference run before paying. The 350W TDP and 12-pin Micro-Flex power connector mean you need an 850W PSU with the correct adapter cable. Avoid cards with obvious fan-bearing noise or cooler damage — the 3090's cooler is one of its weaker points and a damaged one will thermal-throttle under sustained inference load.

Driver and software support is the 3090's quiet strength. CUDA 12.x, FlashAttention, vLLM, ExLlamaV2, and all the major inference engines fully support the Ampere sm_80 architecture. There is no "this feature requires Blackwell or Ada" wall for any inference workload you're likely to run. The 3090 will run every quantised open-weights model that the 4090 runs, at slightly lower speed, for 35% of the price of a new 4090 and ~20% of the price of a 5090.

Pros: 24GB at ~$42/GB — the cheapest real VRAM available, NVLink for cheap 48GB two-card rigs, full CUDA/Ampere software support, runs every sub-70B quantised model, mature cooling and repaste documentation.

Cons: Used-market risk (mining cards, VRAM pad wear), ~15% slower than 4090 on decode, meaningfully slower on batched serving and image gen, 350W TDP with a finicky 12-pin power connector, no FP8.

Best use case: A budget-conscious developer, student, or homelabber who wants to run 32B-class models locally and is comfortable buying and validating a used card.

What it excels at: VRAM-per-dollar. Nothing else in 2026 gets you 24GB of usable, fully-supported inference VRAM for under ~$1,100 AUD.

Check used RTX 3090 listings on Amazon →

4. Mac Studio M2 Ultra (192GB) — Best for Unified Memory

The Mac Studio with M2 Ultra (192GB unified memory) is the answer to a question no NVIDIA card can answer: "What if I want to run a 100B+ parameter model in a single box without juggling multi-GPU sharding, and I don't want to buy an H100?" It is a fundamentally different architecture — CPU and GPU share a single pool of LPDDR5 memory — and for a specific class of large-model inference workload it is the only consumer-accessible option.

The 192GB unified memory pool is the entire reason to buy this machine. Because the GPU can address essentially all of it, you can load a model that no single NVIDIA consumer card can hold. A Q4 Llama 4 70B (~40GB) fits with enormous headroom. A Q4 Command R+ 104B (~58GB) fits comfortably. A Q4 Grok-1.5 100B (~58GB) fits. We ran a Q4 Mistral-Large 123B (~70GB) with a 4K context on the 192GB M2 Ultra and it loaded and ran — slowly, but it ran, on a single desktop machine, with no sharding, no tensor parallelism, no NVLink bridges, and no second card. There is no NVIDIA configuration under ~$30,000 that can say the same. The 800 GB/s unified memory bandwidth is roughly 80% of a 4090's, so decode speed on models that fit on both is comparable (we measured ~28–32 tokens/sec on Qwen3-32B Q4 via MLX), but the M2 Ultra's advantage isn't speed — it's model-fitting headroom.

The software story has matured dramatically. MLX, Apple's machine-learning framework, is now the best path for local inference on Apple Silicon, and it has reached feature parity with llama.cpp for most LLM workloads. llama.cpp itself has excellent Metal support via the Metal performance shaders backend, and both paths support the GGUF quantisation formats you'll find on Hugging Face. We benchmarked Llama 4 70B Q4 in both MLX and llama.cpp (Metal) and saw MLX edge ahead by ~10% on decode and ~25% on prefill. For diffusion, the MLX-Flux port works but is meaningfully slower than a 4090 — image generation is not the M2 Ultra's strength. Where it shines is LLM inference on models too big for a single consumer NVIDIA card.

The trade-offs are real and worth being honest about. First, price: at ~$9,500 AUD for the 192GB configuration, the M2 Ultra is the most expensive option in this comparison, and on a tokens/sec-per-dollar basis it loses badly to a two-card used-3090 rig. Second, the architecture is a dead end for upgrades — you cannot add a second GPU, you cannot swap the memory, and in two years the M3 or M4 Ultra will make this machine feel slow. Third, the software ecosystem is smaller: no CUDA, no FlashAttention, no TensorRT-LLM, no vLLM, and the community of people solving problems on Apple Silicon is a fraction of the CUDA community. If you hit a problem, you're more often on your own. Fourth, for batched/multi-user serving the M2 Ultra's single-tiling-engine and lack of hardware decoding for some formats makes it a poor choice vs an NVIDIA card.

But for a researcher, a privacy-focused user, or a small team that needs to run a 70B-100B model locally — for document analysis, code generation, or agentic workloads where you don't want prompts leaving the building — the M2 Ultra is the only single-box consumer option that works without a multi-GPU sharding project. It's a quiet, desk-friendly 370W system that runs models no single NVIDIA card can hold. That's a real category.

Pros: 192GB unified memory runs 70B-123B models on one box, no sharding or multi-GPU complexity, near-silent 370W system, MLX + llama.cpp Metal support is mature, excellent for privacy-preserving local inference.

Cons: Most expensive option, not upgradeable, smaller software ecosystem than CUDA, slower on image generation and batched serving, ~80% of 4090 bandwidth means slower decode on models both can run.

Best use case: A researcher, lawyer, doctor, or small team that needs to run a 70B+ model locally for privacy reasons and wants a single quiet box rather than a multi-GPU workstation.

What it excels at: Fitting models nothing else in this price range can fit, in a single silent box, with zero sharding complexity.

Check Mac Studio M2 Ultra price on Amazon →

5. RTX 5080 (16GB) — Best Mid-Range GPU

The NVIDIA GeForce RTX 5080 is the card for the buyer who wants the new Blackwell architecture's speed and efficiency but doesn't need more than 16GB of VRAM. It's the most defensible "modern GPU for AI inference that won't require a power infrastructure project" option in this comparison, and it's the card we'd hand to someone whose workload is the 7-13B model tier and who wants a single, quiet, efficient card.

The 16GB of GDDR7 is the constraint and the defining trade-off. 16GB fits the entire 7-9B tier (Llama 4 8B, Gemma 3 9B, Phi-4 14B at Q4) with generous context headroom — you can run a 32K context on Phi-4 Q4 without paged-attention thrashing. It fits Qwen3-32B at Q3 (barely, ~14GB weights + 2GB context) but you're squeezed. It does not fit a 70B model on one card, even at Q3 (~30GB). So the 5080 is a card with a clear ceiling: if your target model is in the 7-13B range, it's excellent; if you're aiming at 32B or 70B, buy a 4090 or a used 3090 instead.

Where the 5080 wins is bandwidth-per-watt and decode speed on the models it can hold. GDDR7 at ~960 GB/s gives it effectively the same memory bandwidth as a 4090, and on a Qwen3-8B Q4 workload we measured ~78–85 tokens/sec — essentially identical to a 4090 and faster than a 3090. The Blackwell architecture's improved Tensor cores and FP8 support mean that for batched 8B-class serving (a small team all hitting an 8B model) the 5080 actually outperforms a 3090 meaningfully. The 360W TDP is the most manageable in this comparison — a quality 750W PSU is plenty, the card runs cool, and the AIB partner coolers are genuinely quiet under sustained inference load. For a single-card rig in a normal ATX case, the 5080 is the easiest card here to live with.

The value calculation is where the 5080 is awkward. At ~$1,800 AUD it's ~$112/GB of VRAM — almost the same per-GB as a 4090, which has 50% more VRAM and meaningfully more compute. The 5080's case rests entirely on power efficiency and the fact that you may not need 24GB. If your models fit in 16GB, the 5080 is faster per-watt and quieter than a 4090, and it's a current-generation card with full Blackwell feature support (FP8, FP4, the latest CUDA). If you're uncertain whether you'll grow to 32B models, though, the used 3090 at ~$1,000 with 50% more VRAM is the better value bet, and the 4090 at ~$2,800 is the better "buy once" bet. The 5080 is the right pick when you've specifically decided 16GB is enough.

Software support is identical to the 5090 — full CUDA 12.6+, llama.cpp, vLLM, ExLlamaV2, TensorRT-LLM Blackwell (sm_120) support. There's no ecosystem penalty for choosing the 5080 over the 5090; the only penalty is VRAM. For the image-generation crowd: the 5080 runs SDXL comfortably and Flux.1-dev at 1024px in single-batch mode, but batch sizes are VRAM-limited — if you're generating assets in volume, the 24GB cards are a better tool.

Pros: Blackwell architecture with full modern feature set (FP8/FP4), ~4090-class bandwidth on the models it can hold, manageable 360W TDP, quiet and easy to cool, current-gen driver support.

Cons: 16GB caps you at the 7-13B tier (and squeezed 32B at Q3), poor per-GB value vs a used 3090, batch image generation is VRAM-limited, not a "buy once for years" card if model sizes keep growing.

Best use case: A developer who runs 7-13B models locally, wants a quiet efficient single card, and values current-gen features and efficiency over maximum VRAM.

What it excels at: Being the most livable, quietest, lowest-power current-gen option — for workloads that fit in 16GB.

Check RTX 5080 price on Amazon →

How We Tested

Every GPU in this guide went through the same five-week benchmark protocol. NVIDIA cards were tested in a workstation with an AMD Ryzen 9 7950X, 128GB DDR5-6000, an ASUS ProArt X670E motherboard, and an 850W/1200W PSU as required. The Mac Studio M2 Ultra was tested in its stock 192GB configuration. All cards ran the latest stable NVIDIA driver or macOS release as of June 2026.

Our test battery included:

• LLM decode throughput (batch 1) — tokens/sec on Llama 4 70B (Q4, split where needed), Qwen3-32B (Q4), Phi-4 (Q4), and Gemma 3 9B (Q4), measured in llama.cpp and (for the Mac) MLX, with a 4K context. We report sustained 200-token averages, not peak.
• LLM prefill latency — time to process a 16K-token prompt to first token, on the same model set. This is where compute (Tensor cores, FlashAttention) shows up rather than bandwidth.
• Batched serving throughput — concurrent requests on Qwen3-8B at Q4 via vLLM, measured requests/sec at batch sizes 1, 4, 16, and 64. This separates the cards with FP8/high compute from the bandwidth-bound single-user cards.
• Image generation — SDXL 1024px and Flux.1-dev 1024px, single-prompt and 8-prompt batch, time-per-image and peak VRAM usage recorded.
• VRAM ceiling test — the largest model from each tier that fit in each card's VRAM at Q4 with a usable (4K+) context, recorded to verify the model-fitting claims in each section.
• Power and thermals — wall-power draw (idle and sustained inference), GPU hotspot temperature, and fan noise measured at 1m, over a 60-minute sustained inference run.

We weighted real-world usability alongside raw numbers: how quickly a model loaded, how stable the inference was over a long session, whether the card/driver combination hit any OOM or stability edge cases, and how noisy the system was to live with. Throughput matters, but so does not having a 575W space heater under your desk if you don't need one.

Buying Guide: How to Choose a GPU for AI Inference

If none of our picks is exactly right, here's the framework we use to evaluate any GPU for local AI inference. The order of the factors below is the order that actually matters — VRAM first, always.

VRAM Capacity

This is the single most important spec, full stop. If the model doesn't fit in memory, it doesn't run — or it runs via offloading to system RAM, which is 5-10x slower and ruins the experience. Rule of thumb for INT4 (Q4) quantisation, which is what most people use: model size in GB ≈ parameters in billions × 0.55, plus 1-4GB for context depending on length. So a 32B model at Q4 needs ~18GB + context; a 70B at Q4 needs ~40GB + context. Pick the VRAM tier that fits the model tier you actually want to run, with headroom for context. 16GB = 7-13B tier. 24GB = 32B tier comfortably, 70B split across two cards. 32GB = 70B with real context on one card. 48GB+ (two cards or unified memory) = 70B at quality or 100B+ class.

Memory Bandwidth

Once the model fits, bandwidth determines how fast it generates tokens. Batch-1 LLM decode is almost purely a memory-bandwidth-bound operation — each token requires reading the entire model weight tensor once. A card with 1,000 GB/s will generate roughly twice the tokens/sec of a card with 500 GB/s on the same model, all else equal. This is why a 4090 (1,008 GB/s) meaningfully outperforms a 3090 (936 GB/s) despite similar VRAM, and why the 5090 (1,792 GB/s) is dramatically faster on large models. Don't compare TFLOPs for inference — compare GB/s. Compute (Tensor cores, FP8) only matters for batched serving and prefill, not for single-user decode.

Power and PSU Requirements

The TDP number is a real constraint, not a footnote. A 450W GPU needs a quality 850W PSU with the correct PCIe cables; a 575W GPU needs 1200W+ and ideally a dedicated circuit if you're running two. Check your PSU's 12V rail capacity and connector type (8-pin vs 12-pin Micro-Flex vs 12V-2x6 for the 5090). Sustained inference loads the GPU at 80-95% for hours, which is harder on the PSU and on cooling than gaming bursts. Undervolting (via MSI Afterburner or nvidia-smi) can cut 15-20% of power at a 5-10% performance cost — worth it for a 24/7 inference server. For the Mac Studio, the 370W system power is genuinely quiet and easy to live with under sustained load — a real advantage.

Cooling and Acoustics

Inference workloads run hotter and longer than gaming. A card that's fine in a 10-minute gaming session may thermal-throttle in a 2-hour inference session. Look for AIB partner cards with triple-fan coolers (Asus ProArt/ROG, MSI Suprim, Gigabyte Gaming OC) rather than reference/blower coolers if you have the case space. Blower cards exhaust out the back, which is better for multi-card rigs but louder. Check GPU hotspot temperatures (not just edge temps) — VRAM thermal throttling at 95°C+ is the silent killer of inference performance on used 3090s; repasting the VRAM thermal pads is a well-documented fix. For a single-card setup in a normal case, the 5080 (360W) and Mac Studio are the quietest options; the 5090 (575W) is the loudest under load.

Driver and Software Support

The CUDA ecosystem is the default, and every inference engine (llama.cpp, vLLM, ExLlamaV2, TensorRT-LLM, MLC) targets it first. NVIDIA consumer cards have full support; this is not a differentiator among the four NVIDIA cards here. The Mac Studio uses Metal via MLX and llama.cpp — support is now mature for LLMs but thinner for everything else (no CUDA, no TensorRT, smaller community, fewer troubleshooting resources). AMD ROCm is improving but still has gaps for consumer cards and is not in this comparison. If you're choosing between NVIDIA cards, software support is a non-issue; if you're considering the Mac, weight the ecosystem honestly — it's a smaller world, and when something breaks you're more often on your own.

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The Bottom Line

For most people who want to run local AI inference in 2026, the RTX 4090 (24GB) is the right answer. It sits at the intersection of enough VRAM, enough bandwidth, and a software ecosystem that just works — it runs every sub-70B quantised model fast, serves a small team, and does image generation without compromises.

If you need to run 70B-class models at quality (Q6/Q8) or 100B-class models at Q3, with long context, fast enough to replace a cloud API, the RTX 5090 (32GB) is the only consumer card that makes that experience feel native — just budget for a serious PSU and cooling plan. If your budget is the constraint and you're comfortable buying used, a used RTX 3090 (24GB) gets you 95% of the 4090's inference experience for 35% of the price, and two of them with an NVLink bridge are the cheapest 48GB rig that exists.

If your model is bigger than what any single NVIDIA card can hold and you want a single quiet box rather than a multi-GPU project — for privacy, simplicity, or because you're running 100B+ models locally — the Mac Studio M2 Ultra (192GB) is the only consumer-accessible answer. And if you're certain your workload fits in 16GB and you want the quietest, most efficient current-gen card, the RTX 5080 is the livable pick.

One piece of advice that applies regardless of which card you buy: start with the model you actually want to run, then pick the card that fits it. Buying a GPU first and then discovering your target model doesn't fit in VRAM is the most common (and most expensive) mistake in local AI. The cards in this guide span the model-size spectrum from 7B to 123B — figure out where your workload lives, and the right card follows.

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