Rent a Tesla T4 for the lowest-cost inference per request in 2026. 16 GB GDDR6 at 70 W passive — Whisper.cpp transcription at 7× realtime, YOLOv8n bulk detection at 340 FPS, BGE-large embedding generation at ~1,100 docs/s. Spot floor sits near $0.08/hr, so high-volume batch jobs cost cents per hour of audio or thousands of images. Billed per-minute, paid in BTC, USDT/USDC or CLORE.
T4 is what happens when Nvidia takes 4090-class silicon, doubles the VRAM, adds ECC, locks the clocks for 24/7 operation, and ships it in a passive datacenter form factor.
Six-year-old silicon, but $/inference still beats every modern card on classification, transcription, and embeddings. 70 W passive form factor keeps host costs at floor — and that floor passes straight to renters as the cheapest GPU on the marketplace for high-volume batch work.
SDXL, Flux, Stable Video Diffusion, HunyuanVideo. RT cores 3rd-gen + OptiX make it the fastest non-HBM card for diffusion.
13B–34B QLoRA on a single card. 16 GB ECC means stable long runs without the corruption risk of consumer GDDR6X.
T4 delivers Hopper-class FP8 inference at a fraction of the H100 hourly rate. The right card when latency matters but you're not training from scratch.
// prices are spot-market lows · refreshed every 60 s
Every server is priced by its host. These are the live floors across the marketplace — you'll see hundreds of variants once you're in.
No sales call. No quota request. No three-week procurement. The first four commands are all you need.
Filter the marketplace by T4 48GB, country, GPU count, reliability score, network speed.
Choose a Docker image — PyTorch, vLLM, ComfyUI, Blender — or paste your own.
You get a public endpoint, an SSH key, and Jupyter on port 8888 in under 90 s.
Per-minute billing rounds to the second. Stop the instance and the meter stops with it.
Yes — for low-cost, high-volume inference (Whisper, ResNet, YOLOv8, embeddings) the T4 still wins on $/inference. Six-year-old silicon, but its 70 W passively-cooled form factor keeps host costs low and rental prices ultra-competitive.
An L40S serving Llama-3 70B FP8 with vLLM and continuous batching pushes roughly 3,000-4,500 output tokens/second at batch saturation. At a $0.78/hr spot rate, that lands near $0.05-$0.07 per million output tokens before the 2.5% spot fee. PoH staking knocks the fee in half; reserved spot floors land you closer to $0.04/M. Numbers vary with prompt length and batch shape - benchmark on your traffic.
Yes. The inference tier (T4, L4, L40S, A10) is exactly what vLLM's PagedAttention and continuous batching are tuned for. L40S handles 70B FP8 single-card with KV-cache headroom; A10 and L4 serve 7B-13B at high throughput; T4 covers Whisper, embeddings, and 7B INT8. Pull the official vLLM Docker image, point it at your model, expose port 8000.
L40S has Ada FP8 tensor cores - the same architecture as H100 for inference math, at a fraction of the rental price. L4 also supports FP8. T4 and A10 are pre-FP8 but have INT8 (T4 added INT8 in Turing, A10 in Ampere) and excel at quantized 7B-13B serving. Pick L40S when FP8 throughput matters; pick A10 or T4 when $/request matters more.
On A10 or L4 with vLLM and batch-1, time-to-first-token for a 7B FP16 model lands around 80-150 ms; p99 inter-token latency is 25-40 ms. L40S with FP8 cuts both roughly in half. T4 doubles them. Real numbers depend on prompt length and concurrent batch size - low-batch interactive serving is fastest, high-batch saturation maximizes throughput.
MIG (Multi-Instance GPU) is supported on A100, A30, and H100/H200 - not on L4, L40S, T4, or A10. For consumer-tier multi-tenancy on the inference tier, run multiple model replicas inside a single Docker container or use container-level resource limits. If you need hardware-isolated MIG slices, rent A100 40GB and partition into up to 7 instances.
16 GB Turing at 70 W passive — still the cheapest path to high-volume Whisper, YOLO, and embeddings in 2026.
70 W passive form factor and rock-bottom rental price — transcription cost dips below $0.02 per audio hour.
Read the guide →Massive throughput per dollar on classification/detection — the standard hyperscaler hardware for low-cost CV.
Read the guide →RAG indexing at scale — T4 is the canonical GPU for batch embedding pipelines in 2026.
Read the guide →Side-by-side specs across the inference tier. Click any row to see that GPU.
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