LLM Inference
Deploy Large Language Models on Affordable GPUs
Run Llama 3, Mistral, DeepSeek R1, Qwen, and Phi-4 with auto-scaling and built-in fault tolerance — at up to 60% lower cost than AWS, RunPod, or Lambda Labs.
TL;DR
- →RTX 4090 at $0.49/hr — runs 7B–13B models with 50+ tokens/sec
- →Auto-failover — nodes fail, your inference doesn't
- →vLLM & TGI ready — deploy with any inference server
- →Save $12,000+/year compared to AWS on a typical 24/7 deployment
Why VectorLay for LLM Inference
Running large language models in production means balancing three things: cost, reliability, and performance. Hyperscalers give you reliability but crush you on cost. Bare-metal rentals are cheap but fragile. VectorLay is the first GPU cloud built from the ground up for inference workloads — combining consumer GPU economics with production-grade fault tolerance.
Cost That Makes Sense
An RTX 4090 delivers 83 TFLOPS of FP32 compute and 24GB of GDDR6X VRAM — enough to run most production LLMs. On VectorLay, that's $0.49/hour. On AWS, a comparable A10G instance costs $1.21/hour, and an A100 runs $3.67/hour. For 24/7 inference, the math is simple: VectorLay saves you $5,000–$23,000 per GPU per year.
And there are no hidden fees. No egress charges, no storage surcharges, no load balancer costs. The price you see is the price you pay.
Fault Tolerance Built In
The biggest risk with distributed GPU infrastructure is node failure. VectorLay solves this at the platform level. When a node goes down, your workload is automatically migrated to a healthy node — typically in under 30 seconds. No manual intervention, no PagerDuty alerts at 3 AM. Your inference endpoint stays live.
This is fundamentally different from marketplace providers like Vast.ai or RunPod, where a host going offline means your workload dies and you start from scratch. VectorLay's control plane continuously monitors node health and pre-warms standby capacity so failover is seamless.
Auto-Scaling for Variable Traffic
LLM traffic is bursty. Your chatbot might handle 10 requests per minute at 2 AM and 500 at 2 PM. VectorLay's auto-scaling spins up additional GPU nodes when queue depth increases and scales down when traffic drops — so you're not paying for idle GPUs during off-peak hours.
Supported Models
VectorLay runs any model that fits in a container. Here are the most popular LLMs our users deploy, along with the recommended GPU configuration:
| Model | Parameters | VRAM Needed | Recommended GPU |
|---|---|---|---|
| Llama 3.1 8B | 8B | ~16GB (FP16) | RTX 4090 / RTX 3090 |
| Mistral 7B | 7B | ~14GB (FP16) | RTX 4090 / RTX 3090 |
| DeepSeek R1 (distilled 7B) | 7B | ~14GB (FP16) | RTX 4090 / RTX 3090 |
| Qwen 2.5 14B | 14B | ~28GB (FP16) / ~16GB (GPTQ) | RTX 4090 (quantized) |
| Phi-4 14B | 14B | ~28GB (FP16) / ~16GB (GPTQ) | RTX 4090 (quantized) |
| Llama 3.1 70B | 70B | ~140GB (FP16) | 2× H100 / 4× A100 |
| DeepSeek R1 671B | 671B | ~1.3TB (FP16) | 8× H100 (FP8/AWQ) |
Not sure which GPU you need? As a rule of thumb: if your model has fewer than 13 billion parameters, an RTX 4090 will handle it comfortably with room for KV cache. For 7B models on a budget, an RTX 3090 at $0.29/hour is hard to beat. For 70B+ models, you'll want H100 or A100 GPUs with tensor parallelism.
GPU Recommendations by Model Size
RTX 3090 — Small Models (≤7B)
24GB VRAM, Ampere architecture. Perfect for Mistral 7B, Llama 3 8B, and fine-tuned 7B models in production. At $0.29/hour, this is the cheapest way to run an LLM in the cloud.
RTX 4090 — 7B–13B Models
24GB VRAM, Ada Lovelace architecture with significantly higher memory bandwidth. Handles 7B models at exceptional speed and 13B quantized models comfortably. The sweet spot for most production LLM deployments.
H100 / A100 — 70B+ Models
80GB HBM3/HBM2e with NVLink support. Required for running 70B+ parameter models at full precision, or for multi-model serving at scale. Tensor parallelism across multiple GPUs for the largest models.
Performance Benchmarks
Real-world inference speed depends on model size, quantization, batch size, and sequence length. Here are representative benchmarks using vLLM on VectorLay GPUs with a batch size of 1 and 512-token context:
| Model | GPU | Quantization | Tokens/sec |
|---|---|---|---|
| Llama 3.1 8B | RTX 4090 | FP16 | ~55-65 |
| Llama 3.1 8B | RTX 3090 | FP16 | ~35-45 |
| Mistral 7B | RTX 4090 | FP16 | ~60-70 |
| Qwen 2.5 14B | RTX 4090 | GPTQ 4-bit | ~30-40 |
| DeepSeek R1 7B | RTX 4090 | FP16 | ~55-65 |
| Llama 3.1 70B | 2× H100 | FP16 | ~25-35 |
Benchmarks are approximate and based on single-request latency. Throughput improves significantly with batched requests using continuous batching (vLLM, TGI).
How to Deploy an LLM on VectorLay
Getting an LLM running on VectorLay takes about 5 minutes. Here's the process:
Create an Account
Sign up at vectorlay.com/get-started. No credit card required for your free tier.
Choose Your GPU
Select the right GPU for your model. RTX 4090 for 7B–13B models, RTX 3090 for budget 7B deployments, or H100/A100 for 70B+. Check pricing for current rates.
Deploy Your Container
Use a pre-built template (vLLM, TGI, Ollama) or bring your own Docker image. Specify the model name and any launch parameters. VectorLay handles GPU passthrough, networking, and storage.
Configure Scaling (Optional)
Set min/max replicas and target queue depth. VectorLay auto-scales based on request volume — scale to zero during quiet periods, burst to dozens of GPUs during peak.
Hit Your Endpoint
You get an HTTPS endpoint compatible with the OpenAI API format. Point your application at it and start generating tokens. Failover, load balancing, and health checks are handled automatically.
LLM Inference Pricing: VectorLay vs. the Rest
Here's what it actually costs to run a 7B LLM 24/7 on different platforms. We compare GPUs with similar capability (24GB VRAM class):
| Provider | GPU | $/hour | $/month (24/7) | Failover? |
|---|---|---|---|---|
| VectorLay | RTX 4090 | $0.49 | $353 | Yes |
| VectorLay | RTX 3090 | $0.29 | $209 | Yes |
| RunPod | RTX 4090 | $0.74 | $533 | No |
| AWS | A10G (24GB) | $1.21 | $871 | Manual |
| Lambda Labs | A10 (24GB) | $0.75 | $540 | No |
| AWS | A100 (40GB) | $3.67 | $2,642 | Manual |
The savings compound at scale. Running 4 GPUs for a multi-model inference pipeline? That's over $2,000/month saved compared to RunPod, and over $8,000/month compared to AWS. For a detailed breakdown, see our GPU cloud pricing comparison.
Annual Savings: Running Llama 3 8B 24/7
Why Consumer GPUs Beat Data Center GPUs for Inference
There's a common misconception that you need enterprise hardware (A100, H100) for production LLM inference. The truth is more nuanced:
A100s and H100s are essential for training — where NVLink, HBM bandwidth, and multi-GPU communication matter. For inference, paying 7× more for a bigger GPU is usually wasted money.
Common LLM Inference Use Cases
AI Chatbots & Assistants
Deploy custom chatbots powered by fine-tuned Llama or Mistral models. Full control over system prompts, response style, and data privacy.
RAG Pipelines
Retrieval-augmented generation with your own knowledge base. Run the embedding model and LLM on the same GPU for minimal latency.
Code Generation
Self-hosted code completion with DeepSeek Coder, CodeLlama, or StarCoder. Keep your code private — no data leaves your deployment.
Content Generation
Batch content creation, summarization, translation, and extraction. Process thousands of documents per hour at a fraction of API costs.
Start running LLMs for less
Deploy your first model in under 5 minutes. No credit card required. No egress fees. No surprise bills. Just fast, affordable LLM inference with built-in reliability.