Best GPU for AI Inference Under $800 (2026)

Best GPU for AI Inference Under $800 (2026)

Photo by Kevin Ku on Unsplash

Quick Answer: The best AI inference GPU under $800 in 2026 is a used RTX 3090 — $700-850 gets you 24GB of VRAM and 936 GB/s of bandwidth, enough to run 32B models at Q4 entirely on-GPU at 25-35 tokens/sec. If you insist on new with warranty, the RTX 4070 Ti Super 16GB (~$750) is the pick; on a tight budget, a used RTX 4060 Ti 16GB (~$380) runs 14B models comfortably. Buy VRAM first, compute second — a model that doesn't fit is infinitely slow.

On This Page

• The One Rule: VRAM First
• The Picks: Five GPUs Under $800
• What Fits in 12GB, 16GB, and 24GB
• Tokens Per Second: What to Actually Expect
• New vs Used: The Real Tradeoffs
• CUDA vs ROCm vs Everything Else in 2026
• Frequently Asked Questions

The One Rule: VRAM First

Inference is a memory game, not a compute game. Two facts drive every recommendation in this article:

1. The model must fit in VRAM, or performance falls off a cliff. Offloading layers to system RAM over PCIe drops you from 30 tokens/sec to 2-5. A "slower" GPU with more VRAM beats a "faster" GPU that has to offload, every single time.
2. Token generation speed scales with memory bandwidth, not TFLOPS. Generating each token requires reading essentially the whole model from VRAM. That's why the 2020-era RTX 3090 (936 GB/s) out-generates the newer RTX 4070 Ti Super (672 GB/s) once both fit the same model.

So the buying order is: VRAM capacity → memory bandwidth → software ecosystem → raw compute. Compute matters mostly for prompt processing (prefill) and batch serving — real, but secondary for a personal inference box.

"For local LLM work, a used 24GB card from 2020 remains a better purchase than any new 16GB card at the same price. Capacity is destiny." — SemiAnalysis, Q1 2026

The Picks: Five GPUs Under $800

Rank	GPU	VRAM	Bandwidth	Street Price (2026)	Verdict
🥇 Flagship	RTX 3090 (used)	24GB GDDR6X	936 GB/s	$700-850	The 24GB king. Runs 32B Q4 fully on-GPU.
🥈 Best new	RTX 4070 Ti Super	16GB GDDR6X	672 GB/s	$720-790 new	Warranty, efficiency (285W), strong prefill.
🥉 Budget	RTX 4060 Ti 16GB (used)	16GB GDDR6	288 GB/s	$360-420	Slow bandwidth, but 16GB at half the budget.
Contender	RX 7900 XT (used/new)	20GB GDDR6	800 GB/s	$550-650	Great hardware; ROCm caveats apply.
Wildcard	Intel Arc B580 12GB	12GB GDDR6	456 GB/s	$250-280 new	Absurd value for 8B-14B models via Vulkan/IPEX.

Why the used RTX 3090 wins

It's the only way to get 24GB of CUDA-backed VRAM under $800 in 2026 (the 4090 still trades at $950-1,100 used; the 5090 is $2,000+). 24GB is the threshold that unlocks 32B-class models — Qwen 3 32B, and 30B-class MoE models at Q4 — which are the smallest models that feel genuinely close to frontier-hosted quality for coding and reasoning. It also takes 24GB LoRA fine-tuning jobs the 16GB cards can't. Watch for mining-era units: test memory junction temps and run an OCCT VRAM pass before paying — our used GPU inspection guide covers the full checklist.

When the others make sense

• RTX 4070 Ti Super: you want a warranty, a 285W power budget instead of 350W, and mostly run 8-14B models fast. AD103 prefill speed makes it feel snappier on long prompts than the 3090 despite generating slower.
• RTX 4060 Ti 16GB: the bandwidth (288 GB/s) is genuinely the bottleneck — expect roughly a third of a 3090's generation speed. But at ~$380 it leaves budget for RAM and a good PSU, and 16GB fits 14B Q4 with long context.
• RX 7900 XT: on paper it embarrasses the 4070 Ti Super (20GB, 800 GB/s, cheaper). In practice you're signing up for the ROCm section below. Fine for llama.cpp/Ollama users; risky for anything exotic.
• Arc B580: llama.cpp Vulkan and Intel's IPEX-LLM stack matured a lot through 2025. At $250 it's the best tokens-per-dollar for 8B models on the market — as a secondary or starter card.

What Fits in 12GB, 16GB, and 24GB

Rule of thumb at Q4 quantization: model weights ≈ params × 0.55-0.6 GB per billion, plus 1-3GB+ for KV cache depending on context length.

VRAM	Fits Comfortably (Q4, 8-16K ctx)	Tight/Max (Q4, short ctx)	Out of Reach
12GB (B580, 4070)	Llama 3.2/3.1 8B, Qwen 3 8B, Gemma 3 12B (barely), Phi-4 14B at Q3	14B Q4 with 4K ctx	32B+ anything
16GB (4070 TiS, 4060 Ti 16)	Qwen 3 14B, Phi-4 14B, Gemma 3 12B + long ctx	Qwen 3 32B at Q2 (not worth it), 24B Q4	32B Q4, 70B
20GB (7900 XT)	Everything above + Mistral Small 24B Q4	32B Q4 with 2-4K ctx	70B
24GB (3090)	Qwen 3 32B Q4, 24B Q5, 14B at FP8 with huge ctx	Llama 4 Scout Q2 partial offload, 70B Q4 w/ heavy offload (slow)	70B fully on-GPU

Notes that save people pain:

• KV cache grows linearly with context. A 32B Q4 model at 32K context can add 4-6GB — the difference between fitting and OOM on a 24GB card. Use Q8 KV cache quantization (a llama.cpp/Ollama flag) to halve it with negligible quality loss.
• MoE models are friendlier than their headline size. Qwen 3 30B-A3B activates ~3B params per token: it needs the VRAM of a 30B for weights but generates at small-model speed — a perfect 3090 workload.
• Leave 0.5-1GB for the desktop if the card also drives your monitors.

Photo by Christian Wiediger on Unsplash

Tokens Per Second: What to Actually Expect

Real-world llama.cpp/Ollama generation numbers (Q4_K_M, single user, mid-2026 builds):

Model	RTX 3090	4070 Ti Super	4060 Ti 16GB	RX 7900 XT	Arc B580
Llama 3.1 8B	90-110 t/s	75-90 t/s	38-45 t/s	80-95 t/s	45-55 t/s
Qwen 3 14B	50-60 t/s	42-50 t/s	20-25 t/s	45-55 t/s	22-28 t/s
Mistral Small 24B	30-38 t/s	won't fit Q4+ctx	won't fit	28-35 t/s	won't fit
Qwen 3 32B	25-32 t/s	—	—	18-24 t/s (tight)	—

For context: 20+ tokens/sec feels fluid for chat; 10-15 is usable; under 8 feels sluggish for interactive work but is fine for batch jobs. Anything reading these tables for coding-assistant use should target the 14B-32B rows — quality below 14B drops off hard for real coding tasks.

Prompt processing (prefill) is where newer architectures claw back ground: the 4070 Ti Super chews through a 16K-token prompt roughly 30-40% faster than a 3090. If your workload is long-document Q&A or RAG over big contexts, weight that accordingly — our local RAG stack guide goes deeper on prefill-heavy workloads.

New vs Used: The Real Tradeoffs

Factor	Used 3090 ($750)	New 4070 Ti Super ($750)
VRAM	24GB	16GB
Warranty	None (card is 5-6 years old)	3 years
Power draw	350W (undervolt to ~280W)	285W
Transient spikes	Nasty (Ampere) — want 850W PSU	Mild — 750W fine
Failure risk	Fans/pads aging; VRAM abuse possible	Effectively zero
Resale in 2 years	~$500-550 (24GB holds value)	~$450-500
Model ceiling	32B class	14B class

The honest framing: the used 3090 is a capability bet; the new card is a reliability bet. If your budget can't absorb a dead $750 card, buy new. If you're comfortable testing before buying (FurMark + OCCT VRAM + memory junction temps under 100°C), the 3090's extra 8GB is worth more than the warranty — it's the difference between running 32B models and not running them at all.

One hybrid path worth mentioning: buy the $380 used 4060 Ti 16GB now, learn your actual workload, and upgrade to a used 4090 when they dip below $800 (likely late 2026 as 5080 Super supply normalizes).

CUDA vs ROCm vs Everything Else in 2026

The software gap has narrowed — but not closed:

• CUDA (NVIDIA): everything works, first, always. llama.cpp, vLLM, ExLlamaV3, TensorRT-LLM, ComfyUI, WhisperX, every fine-tuning framework, every research repo's day-one release. FlashAttention-3, FP8, and speculative decoding land here months before anywhere else. This ecosystem tax is why a worse-on-paper NVIDIA card usually wins.
• ROCm (AMD): dramatically better than 2023. ROCm 6.x officially supports RDNA3 consumer cards on Linux; Ollama and llama.cpp treat AMD as first-class; vLLM works on RDNA3 with caveats. What still bites: Windows support runs through less-mature paths (Vulkan is often faster than ROCm-on-WSL), image/video-gen tooling breaks more often, day-one model support lags, and niche quantization formats (EXL3, some AWQ kernels) may never arrive. If your plan is "Ollama + Open WebUI on Linux," the 7900 XT is genuinely fine. If your plan involves the words "I saw this new repo on GitHub yesterday," pay the NVIDIA tax.
• Intel (IPEX-LLM/Vulkan/SYCL): the surprise of 2025-2026. llama.cpp's Vulkan backend made Arc cards legitimately good at mainstream GGUF inference. Ecosystem depth is still shallow — treat Arc as an inference appliance, not an AI workstation.

Bottom line: under $800 in 2026, the used RTX 3090 remains the answer to the question most people are actually asking. Buy it, undervolt it to 280W, run Qwen 3 32B Q4, and enjoy near-frontier local AI for the price of a mid-range gaming card.

Related Reads

• Best GPU for Local LLM in 2026 (RTX 4090, 5090, Used Options)
• RTX 5090 vs Used RTX 4090 for AI: Price-Performance 2026

Key Takeaways

• Prioritize VRAM over compute: 24GB (RTX 3090) fits 32B models at Q4 quantization, while 16GB (RTX 4070 Ti Super) maxes out at 14B—offloading to system RAM drops speed from 30+ to 2-5 tokens/sec.
• Used RTX 3090 ($700-850) is the best value for 24GB CUDA VRAM, but test for mining damage (OCCT VRAM pass, memory junction temps <100°C) before buying—undervolt to ~280W to reduce power spikes.
• For new GPUs with warranty, the RTX 4070 Ti Super ($720-790) is the top pick, offering 16GB VRAM and 672 GB/s bandwidth; its AD103 prefill speed outperforms the 3090 on long prompts despite slower generation.
• Bandwidth dictates token speed: RTX 3090 (936 GB/s) generates 25-35 tokens/sec on 32B models, while RTX 4060 Ti 16GB (288 GB/s) delivers only 20-25 tokens/sec on 14B models—acceptable for chat but sluggish for coding.
• AMD RX 7900 XT (20GB, 800 GB/s) is a strong hardware contender but requires Linux + ROCm for stability; Intel Arc B580 ($250) is the best budget option for 8B-14B models via Vulkan/IPEX, but lacks CUDA ecosystem support.
• Pair your GPU with 32GB+ system RAM to avoid bottlenecks during model loading and KV cache spills—64GB is ideal for 24GB+ GPUs running long-context workloads.