Best Consumer GPU for AI in 2026: VRAM, Model Fit and Value
Which consumer GPU to buy for local AI in 2026 — RTX 5090, RTX 5070 Ti, RX 9070 XT, Arc B580 and used RTX 3090/4090 compared by VRAM, model fit, software support and launch MSRP.
Direct answer
There is no single "best consumer GPU for AI" in 2026 — there is a best GPU per budget and per model size. VRAM capacity is the hard ceiling: it decides which models load at all. Memory bandwidth and software stack decide how fast they run. Price decides which ceiling you can afford.
The short version:
| Budget | Best pick | Why | Launch MSRP |
|---|---|---|---|
| No hard ceiling | NVIDIA RTX 5090 (32 GB) | The only single consumer card that runs 32B-class models at INT4 with real headroom and approaches 70B Q4 comfort | $1,999 (Jan 2025) |
| ~$750–1,000 | RTX 5070 Ti (16 GB) or RTX 5080 (16 GB) | 16 GB is the practical floor for 14B–22B models at INT4; full CUDA ecosystem | $749 / $999 (Feb 2025) |
| ~$600, AMD-OK | Radeon RX 9070 XT (16 GB) | 16 GB for $599 launch MSRP — the value play if you accept ROCm's smaller footprint | $599 (Mar 2025) |
| ~$250 entry | Intel Arc B580 (12 GB) | 12 GB at $249 — fits 7B–9B INT4 models; weakest software support of the three vendors | $249 (Dec 2024) |
| Used market | RTX 3090 (24 GB) or RTX 4090 (24 GB) | 24 GB is the sweet spot for 32B-class INT4; both discontinued but plentiful used | $1,499 / $1,599 launch (2020 / 2022) |
If you only remember one rule: buy the most VRAM you can afford, then check the software stack runs on your OS. A fast card with too little VRAM cannot load the model; a slow card with enough VRAM usually can.
What "best for AI" actually measures
Three things, in order:
- VRAM capacity — weights + KV-cache + runtime overhead + a fragmentation margin must all fit. Our VRAM calculator runs this per model; the full tier breakdown is in which LLMs fit on 8/16/24/48/80 GB GPUs. What VRAM an LLM needs explains why it is a memory question, not a parameter-count question.
- Memory bandwidth — after the model loads, tokens-per-second tracks bandwidth far more than core count. 1,792 GB/s (RTX 5090) vs 456 GB/s (Arc B580) is the difference between usable and frustrating on the same model.
- Software stack — CUDA runs everything. ROCm runs most things on Linux, a shorter list on Windows. Vulkan is the fallback that works everywhere at lower speed. See local LLM serving engines for how engines differ in backend support.
Quantization trades precision for capacity — it is the main lever that lets a 16 GB card load a 14B model, and it is assumed in every fit number below unless stated otherwise.
VRAM decision table: what each tier buys you
Using the same engine numbers as the VRAM tiers article (deterministic engine, batch 1, 4K context, INT4 unless noted), mapped to consumer SKUs that actually exist:
| VRAM tier | What loads at INT4 (Q4) | Does not fit | Consumer cards today |
|---|---|---|---|
| 12 GB | 7B–9B-class (6.9 GB); small image models quantized | 14B (9.9 GB + KV-cache + overhead is tight/over) | Arc B580; RTX 5070; RTX 3060 12GB (used) |
| 16 GB | 14B (9.9 GB); up to ~22B with short context | 32B (20.1 GB) | RTX 5070 Ti; RTX 5080; RX 9070 XT; RX 9070 |
| 24 GB | 32B (20.1 GB); FLUX.1 dev at FP16 (24 GB) | 70B (40.8 GB) | RTX 4090 (used); RTX 3090 (used); RX 7900 XTX (used) |
| 32 GB | 32B with long-context headroom; comfortable 24B + KV-cache | 70B Q4 (40.8 GB) — marginal at best | RTX 5090 |
| 48 GB | 70B/72B Q4 (40.8 / 42.4 GB) | FP16 70B | No current consumer card — workstation RTX 6000 Ada / L40S class |
Two traps worth calling out:
- 12 GB is a hard wall for 14B-class models at usable context. Qwen 2.5 14B at INT4 is 9.9 GB of weights before KV-cache; with KV-cache growth and runtime, 12 GB runs out of room for long conversations. 16 GB is the real entry point for 14B.
- 24 GB does not run Llama 3.3 70B. 40.8 GB at INT4/4K. One 24 GB card cannot hold it, no quant setting fixes that. You need 48 GB+ (one card) or tensor parallelism across cards — tensor parallelism vs pipeline covers the tradeoff.
Run your exact model through the calculator before buying; these tiers are boundaries, not guarantees.
Consumer GPU comparison
Specs below are manufacturer-published; MSRPs are launch prices in USD with launch dates, not current street quotes. Street prices fluctuate by region and retailer — treat MSRP as a reference anchor, not a price check. Current cheapest listings update separately from this article.
| GPU | VRAM / type | Bandwidth | TDP | PSU rec. | PCIe | Launch MSRP | Launch |
|---|---|---|---|---|---|---|---|
| NVIDIA RTX 5090 | 32 GB GDDR7 | 1,792 GB/s | 575 W | 1,000 W | 5.0 | $1,999 | Jan 2025 |
| NVIDIA RTX 5080 | 16 GB GDDR7 | 960 GB/s | 360 W | ~850 W | 5.0 | $999 | Jan 2025 |
| NVIDIA RTX 5070 Ti | 16 GB GDDR7 | 896 GB/s | 300 W | 750 W | 5.0 | $749 | Feb 2025 |
| NVIDIA RTX 5070 | 12 GB GDDR7 | 672 GB/s | 250 W | 650 W | 5.0 | $549 | Feb 2025 |
| AMD RX 9070 XT | 16 GB GDDR6 | 640 GB/s | 304 W | 750 W | 5.0 | $599 | Mar 2025 |
| AMD RX 9070 | 16 GB GDDR6 | 640 GB/s | 220 W | 650 W | 5.0 | $549 | Mar 2025 |
| Intel Arc B580 | 12 GB GDDR6 | 456 GB/s | 190 W | 600 W | 4.0 x8 | $249 | Dec 2024 |
| NVIDIA RTX 4090 (used) | 24 GB GDDR6X | ~1,008 GB/s | 450 W | 850 W | 4.0 | $1,599 | Oct 2022 |
| NVIDIA RTX 3090 (used) | 24 GB GDDR6X | 936 GB/s | 350 W | 750 W | 4.0 | $1,499 | Sep 2020 |
| AMD RX 7900 XTX (used) | 24 GB GDDR6 | 960 GB/s | 355 W | 800 W | 4.0 | $999 | Dec 2022 |
| NVIDIA RTX 3060 12GB (used) | 12 GB GDDR6 | 360 GB/s | 170 W | 550 W | 4.0 | $329 | Feb 2021 |
The used 24 GB trio matters because 24 GB remains the practical sweet spot: it loads 32B-class models at INT4 (20.1 GB) with KV-cache headroom, and it is the only consumer-tier capacity that can hold FLUX.1 [dev] at FP16 (24 GB, per our model registry). The RTX 3060 12GB endures as the cheapest 12 GB card and a respectable 7B–9B starter; see its GPU page for the full spec sheet.
NVIDIA vs AMD vs Intel: the software reality
This is where "cheapest per GB" and "best GPU" stop being the same question.
NVIDIA — default choice for AI. CUDA is the baseline every framework assumes. Ollama (verified 2026-10-11) requires compute capability 5.0+ and driver 550+ (570+ for CC 5.0–6.2) — every card in the table above qualifies. vLLM, llama.cpp, TensorRT-LLM, ComfyUI, every fine-tuning guide: all CUDA-first. If your goal is "it works this weekend with zero research," buy NVIDIA.
AMD — real progress, with OS-specific caveats. ROCm officially supports the RX 9070 XT / 9070 on Linux (Ubuntu 24.04.2, 22.04.5, RHEL 9.6/9.5/9.4 per AMD's ROCm 6.4.x requirements) and the RX 7900 XTX broadly. The catch: Ollama's Windows ROCm supported-GPU list currently covers RDNA3 cards (RX 7900 XTX/XT/GRE, 7800 XT, 7700 XT, 7600 XT, 7600) — the RX 9070 series is not on it. RDNA4 Windows users should use Ollama's Vulkan backend or WSL2 until official support lands. If you are Linux-first, RX 9070 XT at $599 launch MSRP for 16 GB is the strongest value in the table. If you are Windows-only and want CUDA-equivalent reliability, NVIDIA is still the safe buy.
Intel — cheapest VRAM, thinnest ecosystem. The Arc B580 gives 12 GB at $249, and Ollama can reach Intel GPUs via Vulkan (or oneAPI on Linux per Intel's dgpu-docs). But CUDA-only tools (TensorRT-LLM, most training stacks, some ComfyUI nodes) simply do not run. Treat B580 as a learning card for 7B–9B inference, not a production platform.
For the hybrid question — when cloud beats a local card — host vs API breakeven runs the numbers.
Recommendations by budget
Under $300 — Arc B580 ($249) or used RTX 3060 12GB. Both land at 12 GB. B580 has more bandwidth (456 vs 360 GB/s) and is new; the 3060 has CUDA and a decade of community fallback. If any tool you plan to use mentions CUDA, take the 3060. If you only need ollama run on 7B–9B models, the B580 works — expect to spend an evening on driver/Vulkan setup. Starting point: can I run AI locally.
$500–750 — RTX 5070 ($549) or RTX 5070 Ti ($749). The 5070 is 12 GB — same capacity wall as the 3060, but far more bandwidth and every CUDA feature. The jump to 5070 Ti's 16 GB is the real upgrade: 14B-class models stop being tight. If you can stretch to $749, take the 16 GB.
$599 AMD play — RX 9070 XT. 16 GB for the price of a 5070 Ti's competitor, with 640 GB/s. Only if you accept the ROCm/Vulkan situation above. Strong on Linux; verify your exact stack first.
$1,000–2,000 — RTX 5080 ($999) or RTX 5090 ($1,999). The 5080 is a fast 16 GB CUDA card. The 5090 is the only current consumer card with 32 GB — it is what you buy when 32B-class models are the daily driver and you do not want to think about fit margins. Note the 575 W TDP and 1,000 W PSU recommendation: budget for the whole system, not just the card.
Used 24 GB — RTX 3090 or RTX 4090. Both are discontinued at their launch MSRPs ($1,499 / $1,599) and trade on the used market; prices vary widely by condition and region, so we do not quote street figures here. The 4090 is roughly the efficiency and bandwidth step over the 3090; the 3090's advantage is usually availability and price. 24 GB unlocks 32B Q4 and full-fat FLUX.1 dev. Two cards in NVLink-ish multi-GPU (the 3090 supports 2-way NVLink; the 4090 does not) is a separate rabbit hole — see RTX 4090 vs L40S economics for how consumer multi-card setups compare to workstation cards.
Workstation reality check. 70B models at INT4 need 40.8 GB. No current GeForce or Radeon card provides that. The paths are: RTX 6000 Ada (48 GB), L40S (48 GB), used A100 40GB — or two 24 GB cards with tensor parallelism and its overhead. If 70B local is the actual goal, compare the datacenter options before buying consumer twice.
Model-to-GPU examples
Ollama download sizes below are published library sizes (verified 2026-10-11); VRAM totals use our deterministic engine at 4K context, INT4, batch 1. Quantization settings move these — the tiers article has the full precision matrix.
| Model (Ollama tag) | Download size | Engine VRAM @4K INT4 | Minimum sensible card |
|---|---|---|---|
Llama 3.3 70B llama3.3:70b | 43 GB | 40.8 GB | 48 GB (RTX 6000 Ada/L40S class) or 2×24 GB TP |
Qwen 2.5 Coder 32B qwen2.5-coder:32b | 20 GB | 20.1 GB | 24 GB (used RTX 4090/3090) |
Qwen 2.5 32B qwen2.5:32b | 20 GB | 20.1 GB | 24 GB |
Phi-4 phi4 | 9.1 GB | ~9.9 GB w/ KV-cache | 16 GB |
Qwen 2.5 14B qwen2.5:14b | 9.0 GB | 9.9 GB | 16 GB |
Gemma 2 9B gemma2:9b | 5.4 GB | ~6.9 GB | 12 GB |
Qwen 2.5 7B qwen2.5:7b | 4.7 GB | ~5.5 GB | 8–12 GB |
| FLUX.1 dev (image gen) | — | 24 GB FP16 / 12 GB INT4 | 24 GB FP16; 16 GB quantized |
The pattern: one capacity tier up from where the model "technically fits" is the comfortable buy. 14B at 9.9 GB "fits" 12 GB on paper and chokes in a long session; 16 GB handles it. 32B at 20.1 GB "fits" 24 GB and leaves KV-cache room for real context. See top image generation models and GPU sizing for the diffusion side.
Buying checklist
Before the card goes in the cart:
- Match PSU to TDP, not to habit. RTX 5090 wants a 1,000 W PSU per NVIDIA; RTX 4090 and RX 7900 XTX want 850 W / 800 W; RTX 3090 wants 750 W. Transient spikes on modern cards kill marginal units.
- Check your slot. RTX 50-series is PCIe 5.0; RX 9070 series is PCIe 5.0 x16; Arc B580 runs PCIe 4.0 x8. On older boards you lose little for inference, more for multi-GPU.
- Confirm software on your OS before purchase. Windows + AMD RDNA4 ≠ Windows + AMD RDNA3 in Ollama today. If a specific tool (TensorRT-LLM, a training framework) is on your list, check its backend, not the marketing page.
- VRAM capacity first, bandwidth second, cores third. A 16 GB card with 960 GB/s beats a 12 GB card with 672 GB/s for AI every time, even if the 12 GB card has more CUDA cores.
- Plan the model, then the card. Pick the model you actually want to run (model pages show min VRAM), add 10–15% headroom, round up a tier. Or just run it through the calculator.
- Sizing a whole box, not just a GPU? Our methodology documents how every number on this site is derived.
Limitations
- MSRPs are launch prices in USD with launch dates, sourced from manufacturer announcements. They are reference anchors, not live quotes. Street prices vary by region, AIB model, tariff environment, and generation timing — we do not publish fabricated current prices. Live listings: cheapest GPUs.
- VRAM totals assume batch 1, 4K context, INT4, and our standard overhead model (weights + KV-cache + 1.2 GB runtime + 0.4 GB activations + 10% fragmentation headroom). Longer context, larger batches, or FP8/FP16 change the answer — use the calculator.
- Software support statements (Ollama GPU lists, ROCm OS matrices, Vulkan paths) were verified 2026-10-11 against official docs. These lists move; re-check before purchase.
- Used-market prices are intentionally not quoted. RTX 3090 / 4090 / RX 7900 XTX values are condition- and region-dependent; research current listings yourself.
- No benchmark claims (tokens/sec, tok/s deltas) appear in this article. Throughput varies by model, engine (see serving engines), clocks, cooling, and power limits — we report manufacturer bandwidth specs and leave measured performance to dedicated benchmarks.
- When renting beats buying, host vs API breakeven covers the crossover.
Specs and MSRPs verified against manufacturer pages 2026-10-11. Ollama library sizes verified 2026-10-11. ROCm and Ollama GPU-support matrices verified 2026-10-11. Engine VRAM numbers: deterministic canonical engine, run 2026-10-07, cross-referenced with the VRAM tiers article.