24GB GPU Server:
The AI Hosting Sweet Spot
Run 8B–14B LLMs, AI Agents, RAG Pipelines, Stable Diffusion and FLUX on dedicated RTX Pro 4000 GPU VPS, RTX A5000, and RTX 4090 bare metal servers. No cold starts. No shared resources. No surprise bills.
- GPU ModelNVIDIA RTX Pro 4000
- VRAM24GB GDDR7 (Blackwell)
- CPU Cores24 vCores
- RAM56GB DDR5
- Storage320GB NVMe SSD
- Bandwidth500Mbps Unmetered
- PCIe PassthroughYes — 100% Dedicated
Why 24GB VRAM Is the Production Sweet Spot
24GB is the threshold where the most-deployed open-source AI models, image generation pipelines, and rendering workloads all run without compromise — at a fraction of 48GB+ card pricing.
LLM Inference
- Llama 3 / 3.1 / 3.2 8B — FP16, full quality (~16GB)
- Qwen3 14B / DeepSeek R1 Distill 14B — FP8 (~14GB)
- Qwen2.5 32B Q4_K_M — fits in ~20–22GB
- Code Llama 34B Q4 — code agent workloads
- Mistral 7B / Gemma 9B — fast inference with concurrency
AI Image Generation
- Stable Diffusion XL — multiple ControlNet adapters loaded
- Flux.1-dev — full 12GB model with generation headroom
- ComfyUI complex workflows with LoRA + upscaler chained
- AnimateDiff / video generation (lightweight pipelines)
- Batch generation at 1024×1024+ without VRAM swapping
AI Agents & RAG Pipelines
- Multi-agent frameworks (LangGraph, AutoGen, CrewAI)
- RAG pipelines: embedding model + 14B LLM simultaneously
- Document extraction pipelines (1,000+ papers / batch)
- 24/7 autonomous agents — need Always-On dedicated GPU
- Coding assistants and agentic code generation servers
LoRA Fine-Tuning
- LoRA / QLoRA fine-tuning of 7B–13B models
- Domain-specific adapter training (medical, legal, code)
- DreamBooth / image-specific LoRA for SDXL / Flux
- Gradient checkpointing enables larger effective batch sizes
- PEFT workflows with Hugging Face Transformers / Axolotl
3D Rendering
- OctaneRender — RTX 4090 scores 1,271 on OctaneBench
- Blender Cycles — GPU-resident complex scenes up to ~18GB
- V-Ray, Redshift — production scene asset pools
- Unreal Engine 5 Nanite real-time previews
- 2× GPU option (RTX 4090): OctaneBench score ~2,410
Virtual Workstation & Streaming
- Windows GPU RDP — remote creative workstation
- 4K video editing in DaVinci Resolve with GPU effects
- GPU-accelerated live streaming (NVENC AV1)
- Game development testing (Unity / Unreal Engine)
- SideFX Houdini VFX simulations with CUDA acceleration
Recommended 24GB GPU Server Plans
Three dedicated 24GB VRAM configurations — from next-gen Blackwell VPS to bare-metal RTX 4090 and RTX A5000. All include full root access and SOC-certified US datacenter hosting.
Which plan fits your workload?
AI Hosting: LLM APIs, RAG, Agents, Diffusion
- vLLM / Ollama server, RAG pipelines, AI agents 24/7
- Stable Diffusion XL, Flux.1-dev, ComfyUI
- LoRA fine-tuning (7B–13B) · 56GB RAM for multi-service
Rendering, Scientific Compute, Long-Running Jobs
- OctaneRender, Blender, V-Ray (OctaneBench 593)
- Long training runs where ECC memory integrity matters
- 3× A5000 multi-server option available for scale-out
Max CUDA Throughput: Image Gen, Rendering, Video
- Stable Diffusion / Flux batch speed (60–70 it/s SDXL)
- OctaneBench 1,271 — 2.1× faster than A5000
- 4K video editing, game dev, dual-GPU option (2×4090)
| Plans | GPU Model | CPU | Memory | Disk | Bandwidth | Price | |
|---|---|---|---|---|---|---|---|
| Advanced GPU VPS - RTX Pro 4000 | RTX Pro 4000 | 24 CPU Cores | 56GB RAM | 320GB SSD | 500Mbps Unmetered | $159.00/mo | Order Now |
| Enterprise Multi-GPU Dedicated Server - 2xRTX 4090 | 2 x RTX 4090 | 36-Core Dual E5-2697v4 | 256GB RAM | 240GB SSD+2TB NVMe+8TB SATA | 1000Mbps Unmetered | $729.00/mo | Order Now |
| Enterprise Multi-GPU Dedicated Server - 3xRTX A5000 | 3 x RTX A5000 | 36-Core Dual E5-2697v4 | 256GB RAM | 240GB SSD+2TB NVMe+8TB SATA | 1000Mbps Unmetered | $539.00/mo | Order Now |
Enterprise Dedicated GPU Server - RTX 4090 | RTX 4090 | 36-Core Dual E5-2697v4 | 256GB RAM | 240GB SSD+2TB NVMe+8TB SATA | 100Mbps Unmetered | $307.44/mo | Order Now |
| Advanced Dedicated GPU Server - RTX A5000 | RTX A5000 | 24-Core Dual E5-2697v2 | 128GB RAM | 240GB SSD+2TB SSD | 100Mbps Unmetered | $269.00/mo | Order Now |
Performance Benchmarks
Real data from GPU Mart infrastructure. LLM benchmarks measured on RTX Pro 4000 (GPU Mart internal testing, May 2026). Rendering scores from OctaneBench test suite.
| Model | Quant | VRAM Used | Output (tok/s) | Concurrency | Framework |
|---|---|---|---|---|---|
| Llama 3.1 8B Instruct | FP16 | ~16GB | 16–32 | vLLM | |
| Qwen2.5 14B | FP8 | ~14GB | 8–16 | vLLM | |
| DeepSeek R1 Distill 14B | Q4_K_M | ~9GB | 8–16 | Ollama | |
| Mistral 7B Instruct | FP16 | ~14GB | 16–32 | vLLM | |
| Qwen2.5 32B | Q4_K_M | ~20–22GB | 1–4 | Ollama |
Output tokens/second under sustained load. Actual performance varies with prompt length and context window. Full benchmark data: gpu-mart.com/guides/self-hosted-llm
| GPU | SD WebUI (it/s) | ComfyUI (it/s) | Notes |
|---|---|---|---|
| RTX 4090 | Best for SDXL / Flux batch generation | ||
| RTX Pro 4000 | Blackwell FP8 advantage on Flux pipelines | ||
| RTX A5000 | — | — | Comparable to RTX 4090 for rendering workloads |
Benchmarks at 1024×1024, SDXL model, 20 steps, DPM++ 2M Karras. Higher iterations/sec = faster image generation.
| GPU | OctaneBench (1×GPU) | OctaneBench (2×GPU) | Relative to A5000 |
|---|---|---|---|
| RTX 4090 | 2,410 | 2.1× faster than A5000 | |
| RTX A5000 | 1,170 | Baseline · ECC + NVLink | |
| RTX A4000 (16GB) | 690 | Reference (16GB, lower VRAM) |
Dual-GPU scaling is approximately 1.9× single-GPU score. Compatible with Octane, V-Ray, Blender Cycles, Redshift, and other CUDA-based renderers. Full rendering benchmark: gpu-mart.com/gpu-rendering
Real Deployment: 850 Media / FieldMatrix.AI
A bootstrapped AI company running production LLM inference, multi-agent workloads, and a scientific research synthesis pipeline — simultaneously on a single RTX Pro 4000 GPU Mart server.
"If you're a small to mid-size AI company that needs real GPU horsepower without enterprise pricing, Database Mart is the move. We're running production AI inference, multiple autonomous agents, and a research pipeline on a single server — and it handles it all. The hardware is current-gen, the uptime is solid, and the value is exceptional."
— Michael G. Cadenhead, 850 Media / FieldMatrix.AI
Their active workloads on a single RTX Pro 4000 24GB server: Ollama running Llama 3.1 70B (Q3), Qwen 2.5 Coder 32B, and 12+ models for termite research paper extraction and code generation; FieldMatrix Operator — real-time WebSocket vision AI streaming smart glasses camera feeds for field technicians; autonomous AI agents running 24/7 for research automation and podcast production; and a Termite.Help synthesis pipeline processing 1,000+ peer-reviewed scientific papers. All simultaneously, zero downtime.
Price Comparison: 24GB GPU Hosting in 2026
GPU Mart RTX Pro 4000 delivers next-gen Blackwell architecture at a price point where most competitors offer older Ampere-gen 24GB cards. Data collected May 2026.
| Provider | GPU Mart — RTX Pro 4000 | RunPod — RTX A5000 | HostKey — RTX A5000 | Vast.ai — RTX 3090 | AWS (EC2 T4) |
|---|---|---|---|---|---|
| VRAM | 24GB GDDR7 (Blackwell) | 24GB GDDR6 (Ampere) | 24GB GDDR6 (Ampere) | 24GB GDDR6X | 16GB (T4) |
| Monthly Price | $159/mo | $194.40/mo | ~$294/mo | $110–160/mo (variable) | ~$438/mo |
| Bandwidth Fees | Included (check policy) | Included | Charged separately | Per-GB metered | $0.09/GB egress |
| Dedicated Resources | PCIe Passthrough — 100% | Secure Cloud (dedicated) | Dedicated | Third-party host varies | Shared pool |
| Cold-Start Latency | Zero — Always-On | 30s+ (Serverless) | Always-On | Variable | Instance spin-up |
| Data Center | USA · SOC Certified | Multi-region | EU / Global | Unverified third-party | Multi-region |
| Architecture | Blackwell (latest gen) | Ampere | Ampere | Ampere / older | Turing (T4) |
| Support Response | <5 min · 24/7 | Ticket-based | Standard | Community | Enterprise tier required |
Pricing sourced from provider websites, May 2026. Bandwidth policy: please verify current terms at gpu-mart.com/pricing before ordering.
24GB vs 48GB: Choosing the Right VRAM Configuration
The upgrade from 24GB to 48GB matters primarily for specific model sizes and concurrency requirements. Most production teams don't need it — but when you do, it's a hard requirement.
RTX Pro 4000 · RTX A5000 · RTX 4090
Covers the majority of production AI workloads. Best price-to-performance ratio in the GPU Mart lineup.
- Llama 3 / 3.1 / 3.2 8B at full FP16 precision (~16GB)
- Qwen3 14B, DeepSeek R1 Distill 14B at FP8 (~14GB)
- Qwen2.5 32B, Code Llama 34B at Q4_K_M (~20–22GB)
- Stable Diffusion XL, Flux.1-dev, full ComfyUI workflows
- LoRA / QLoRA fine-tuning of 7B–13B models
- AI agents, RAG pipelines, multi-service deployments
- OctaneRender (RTX 4090: 1,271 OctaneBench score)
RTX A6000 · A40 · 2× RTX 4090
Step up when target models genuinely don't fit at acceptable quality, or when concurrency demand exceeds 24GB headroom.
- Qwen2.5 72B, Llama 3.3 70B at Q4 (~40GB) — barely fits on 48GB; for production 70B throughput, 80GB+ (A100/H100) is recommended
- High-concurrency APIs handling 50+ simultaneous requests
- Multi-model serving: two 14B models loaded simultaneously
- Long-context RAG (128K tokens) with high concurrency
- Tensor-parallel fine-tuning of 30B+ models
Frequently Asked Questions
Common questions about 24GB GPU servers, LLM hosting, AI agent deployment, and model compatibility.
- What LLM models run best on a 24GB GPU server?+
- The 24GB sweet spot in 2026: Llama 3.1 8B at FP16 (~16GB, 120–145 tok/s); Qwen3 14B / DeepSeek R1 Distill 14B at FP8 (~14GB, 65–90 tok/s); Mistral 7B at FP16 (~14GB, 95–115 tok/s); Qwen2.5 32B / Code Llama 34B at Q4_K_M (~20–22GB, 28–38 tok/s). Full benchmark data: gpu-mart.com/guides/self-hosted-llm.
- How do I set up a vLLM server or Ollama server on a 24GB GPU VPS?+
- A dedicated 24GB GPU VPS with PCIe Passthrough is the standard configuration for a production vLLM server or Ollama server. The RTX Pro 4000 ships with Ubuntu 22.04, CUDA 12.4, and full root access — install vLLM via pip and serve an OpenAI-compatible endpoint in under 10 minutes. For Ollama, pull any supported model and it runs immediately with GPU offloading. Zero cold-start latency means your inference API responds instantly on every request, unlike serverless platforms that reload models from storage.
- Is a 24GB GPU VPS suitable for RAG server and AI agent deployments?+
- Yes. A production RAG server needs an embedding model (~1–2GB) plus a 7B–14B inference LLM (~14–16GB) in VRAM simultaneously — 24GB handles this with headroom for KV cache. A 24GB GPU VPS with Always-On dedicated resources is also the minimum for reliable AI agent hosting: shared or serverless GPUs interrupt long-running agents when resources are reclaimed. The RTX Pro 4000's 56GB RAM supports concurrent multi-agent frameworks (LangGraph, CrewAI, AutoGen) without CPU memory pressure.
- Which GPU server is best for Stable Diffusion hosting and ComfyUI workflows?+
- 24GB is the practical floor for serious Stable Diffusion hosting. It's the first VRAM tier where SDXL, Flux.1-dev, multiple ControlNet adapters, and an upscaler can all stay loaded simultaneously — eliminating the per-generation model-swap overhead that makes 8–16GB cards slow for complex ComfyUI workflows. RTX 4090: ~60 it/s SD WebUI, ~70 it/s ComfyUI at 1024×1024 SDXL. RTX Pro 4000 VPS: ~35–50 it/s with a Blackwell FP8 edge on Flux pipelines.
- Is the RTX Pro 4000 VPS a true dedicated GPU or shared?+
- True dedicated. PCIe Passthrough allocates the physical GPU fully to your instance — no time-slicing, no vGPU sharing. 100% of the 24GB VRAM is yours with no noisy-neighbor interference. For workloads needing IPMI access or full bare-metal kernel control, the RTX A5000 or RTX 4090 dedicated servers are the right choice.
- How does GPU Mart compare to RunPod for 24GB GPU hosting?+
- GPU Mart RTX Pro 4000 at $159/mo is ~18% less than RunPod's RTX A5000 Secure Cloud ($194.40/mo), while running newer Blackwell architecture. The critical operational difference: GPU Mart is Always-On with zero cold-start vs RunPod Serverless's 30+ second cold-starts on idle sessions — a hard requirement for 24/7 LLM hosting, vLLM servers, and RAG pipelines. GPU Mart runs owned hardware in a SOC-certified US datacenter, not third-party community hosts.
- Can I run Stable Diffusion, an LLM, and an AI agent on the same server?+
- Yes — this is how 850 Media uses their RTX Pro 4000: Ollama (Qwen2.5 32B + multiple 14B models), a real-time vision AI WebSocket application, and autonomous research agents all running simultaneously. 24GB VRAM handles GPU workloads; 56GB RAM supports system processes. Ollama's context-switching is fast enough for multi-service production deployments at team scale with zero downtime in 3+ months of deployment.
- What is the bandwidth policy for 24GB GPU servers?+
- The RTX Pro 4000 VPS includes 500Mbps unmetered bandwidth — no per-GB egress charges. RTX A5000 and RTX 4090 plans vary; verify at gpu-mart.com/pricing. Unlike AWS/GCP ($0.09/GB outbound), GPU Mart has no egress fees — a meaningful saving for AI hosting workloads with frequent large model downloads or high-throughput RAG pipelines.
- Who should choose a 48GB or 80GB GPU server instead of 24GB?+
- Step up when: you need 70B parameter models — those require 80GB+ VRAM for production throughput (48GB barely fits them at Q4, with poor concurrency); your simultaneous request count regularly exceeds 30+ on a 14B model; or you need two large models resident at the same time. For genuine 70B production inference, skip 48GB and go directly to an A100 80GB or H100 80GB server.
Deploy Your 24GB GPU Server Today
RTX Pro 4000 from $159/mo · RTX A5000 from $269/mo · RTX 4090 from $409/mo. Always-On dedicated GPU, SOC-certified US datacenter. No setup fee, no per-token billing.