GPU Mart Technical Team / Updated May 2026 / Pricing verified May 2026
48GB VRAM GPU Servers for
AI Production Workloads
Run 14B–35B LLMs at full precision, Flux, ComfyUI, and multi-model AI stacks on dedicated 48GB VRAM hardware — flat-rate pricing, no cold starts. Teams switching from hourly cloud billing report 30–50% lower monthly costs at 24/7 utilization.
What 48GB Unlocks
What Runs on a 48GB VRAM GPU Server?
48GB is the inflection point where 70B models fit on one card, multi-model stacks stop hitting OOM, and image gen runs at full precision.
Bottom line: a single 48GB VRAM GPU server replaces what previously required a 2–3 GPU cloud setup — at a fraction of the monthly cost.
14B–35B LLMs at Full Precision
48GB is the sweet spot for 14B–35B models. Qwen3 14B, DeepSeek 14B, Gemma 3 12B run at full FP16 with 20+GB remaining for KV cache and concurrency. 4×A6000 (192GB pooled) handles 70B+ at scale. Source: gpu-mart.com/guides/self-hosted-llm.
Full-Precision Image Generation
Flux.1-dev (~24GB BF16), SDXL + multi-ControlNet, ComfyUI — no memory-saving mode. Batch 4+ images simultaneously on a 48GB VRAM server.
Multi-Model Production Stacks
LLM + TTS + image gen + ASR concurrently. Real GPU Mart clients run Qwen 35B (28GB) + CosyVoice TTS (11GB) + ComfyUI on one server, all models resident in VRAM.
3D Rendering
Full scene geometry, HDR maps, and SSS shaders for Blender Cycles, OctaneRender, or V-Ray — no scene splitting, no session limits.
LoRA / QLoRA Fine-Tuning
Fine-tune 7B–13B models in full FP16, or up to 34B with QLoRA, on a single 48GB GPU — no gradient checkpointing tricks required. For 70B full-precision LoRA, a multi-GPU configuration is needed.
ASR & Speech AI
Faster Whisper Large-v2 uses <10GB — leaving 38GB+ for a co-resident LLM or TTS engine with no CUDA contention between services.
Step-by-step: vLLM, Ollama, and LLM stacks on GPU Mart servers.
Self-Hosted LLM Guide →Available Hardware
48GB GPU Servers at GPU Mart
Three GPU architectures covering different performance tiers. All include full root access, local SSD/NVMe, and 100-1000Mbps unmetered bandwidth. Verify latest bandwidth policy at gpu-mart.com.
Advanced GPU VPS - RTX Pro 5000
- GPU Model: RTX Pro 5000
- CPU: 24 CPU Cores
- Memory: 56GB RAM
- Disk: 320GB SSD
- Bandwidth: 500Mbps Unmetered
- IP: 1 Dedicated IPv4
- Location: USA
- Backup: Once per 2 Weeks
Enterprise Dedicated GPU Server - RTX A6000
- GPU Model: RTX A6000
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 100Mbps Unmetered
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Dedicated GPU Server - A40
- GPU Model: A40
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 100Mbps Unmetered
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Multi-GPU Dedicated Server - 3xRTX A6000
- GPU Model: 3 x RTX A6000
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 1000Mbps Unmetered
- IP: 1 Dedicated IPv4
- Location: USA
Model Compatibility
Which Models Fit Best in 48GB VRAM?
48GB is the sweet spot for 14B–27B models at full precision or INT4, and for multi-model stacks. 70B models technically load at heavy quantization but leave minimal KV cache headroom — not recommended for production inference.
| Model | VRAM Required | Precision | Status on 48GB | Recommended Stack |
|---|---|---|---|---|
| Mistral 7B / LLaMA 3.1 8B | ~14–16 GB | FP16 | Ideal — leaves headroom | vLLM / Ollama |
| Qwen3 14B / DeepSeek 14B | ~28 GB | FP16 | Ideal — primary sweet spot | vLLM / Ollama |
| Gemma 3 12B | ~24 GB | FP16 | Ideal — leaves 24GB for KV cache | vLLM / Ollama |
| Qwen3 27B / GPT-OSS 20B | ~22–28 GB | INT4 / FP16 | Good fit — recommended max range | vLLM / Ollama |
| Qwen 3.2 32B | ~18 GB | Q4_K_M | Runs well at INT4 | Ollama / llama.cpp |
| Flux.1-dev (Image Gen) | ~24 GB | BF16 | Full precision, 24GB headroom | ComfyUI |
| SDXL + multi-ControlNet | ~18–24 GB | FP16 | Batch 4+ images | ComfyUI / A1111 |
| Whisper Large-v2 (ASR) | ~10 GB | FP16 | Excellent — runs alongside LLM | Faster Whisper |
| Meta-LLaMA 3 70B | ~43 GB | Q4_K_M only | Possible but not recommended | Ollama (low concurrency) |
| Qwen 72B / DeepSeek 70B | ~45–47 GB | Q4_K_M only | Near VRAM ceiling — minimal KV cache | Upgrade to multi-GPU |
| LLaMA 3 405B | >200 GB | — | Requires multi-GPU | 4×A6000+ (192GB) |
VRAM figures include model weights + KV cache overhead at typical context lengths. Source: GPU Mart internal benchmark data, NVIDIA official specs, gpu-mart.com/guides/self-hosted-llm.
VRAM math, KV cache estimation, and full GPU selection guide.
Self-Hosted LLM Guide →Benchmark Data
Inference Benchmarks: RTX Pro 5000, A6000 & A40
Real-server data on 14B–70B workloads — the primary use case for 48GB. Multi-GPU 4×A6000 data shown for teams scaling beyond a single card. vLLM 0.6.x, CUDA 12.x. Source: databasemart.com benchmark series, Jan–Feb 2026.
RTX Pro 5000 (48GB GDDR7) — vLLM Throughput, Single GPU
Blackwell RTX Pro 5000 (66.94 TFLOPS FP32) excels at 8B–20B models. GPT-OSS 20B INT4 outperforms 8B FP16 in throughput due to lower VRAM pressure per token. Source: databasemart.com/blog/vllm-gpu-benchmark-pro5000 / Jan 2026
RTX Pro 5000 — Ollama Inference (4-bit, Single GPU)
LLaMA 3 70B Q4 loads on a single Pro 5000 (48GB) — demonstrating single-card 70B inference viability. For higher concurrency, upgrade to Pro 6000 GPU VPS, A100 (80GB) or H100 dedicated server. Source: databasemart.com/blog/ollama-gpu-benchmark-pro5000 / Jan 2026
4×A6000 (192GB) vs 4×A100 40GB (160GB) — 72B Models
A 72B model (~137GB) leaves almost no KV cache room in 160GB. 4×A6000's extra 32GB provides ~55GB of KV headroom — ~3× throughput with no faster compute. Source: databasemart.com/blog/vllm-gpu-benchmark-a100-40gb-4 / 2025
Tesla A40 48GB — vLLM at 50 Concurrent Requests
A40 excels at 7B–14B inference at 50 concurrent requests. Memory pressure increases at 12B+ / 100+ concurrent — consider multi-GPU. Source: databasemart.com/blog/vllm-gpu-benchmark-a40
Non-consensus finding: For 70B+ LLM inference, VRAM capacity beats compute TFLOPS. 4×RTX A6000 (192GB) outperforms 4×A100 40GB (160GB) by ~3× on throughput — not because the A6000 is faster, but because a 72B model leaves the A100 config with almost no KV cache room. Benchmark VRAM headroom after model load, not just raw FLOP counts.
Source: databasemart.com/blog/vllm-gpu-benchmark-a100-40gb-4 / GPU Mart internal deployment data, 2025–2026
Production Use Cases
Real Workloads Running on GPU Mart 48GB Servers
Drawn from actual GPU Mart customer deployments on RTX Pro 5000 (48GB) servers.
Private Digital Human Backend
Qwen3.5 35B (AWQ 4-bit, 28GB) via vLLM + CosyVoice 3 TTS (10.9GB) — complete private AI assistant on one server. Active VRAM: ~39GB. Qwen3.5-35B-AWQ + CosyVoice 3 / Docker / vLLM
Multimodal AI Platform
Qwen3 35B VLM (28GB) + ComfyUI (18GB) + Whisper ASR — full AI content pipeline on one 48GB server, handling 190K-char docs with RAG. Qwen3.6-35B-A3B + ComfyUI + Whisper / vLLM
Sports AI — Dual-LLM Live Inference
Qwen3 8B (10GB) + Gemma 3 12B (8GB) simultaneously via llama.cpp — both permanently resident in 48GB, no model-load latency between requests. Qwen3-8B-Q4 + Gemma-3-12B-Q4 / llama.cpp
Enterprise RAG + Private LLM API
vLLM serving Qwen3.5-27B (INT4, 38GB) for enterprise RAG with concurrent API endpoints. Remaining ~10GB for embeddings and context caching. Qwen3.5-27B INT4 / vLLM / Production REST API
Single GPU vs. Multi-GPU
Scale from 48GB to 192GB NVLink
All RTX A6000 configs are bare metal dedicated. Choose by model size and concurrency.
Rule of thumb: single card for 70B Q4 production; 4×A6000 for 70B FP16 at scale; 4×A100 only if your primary models are sub-32B at high concurrency.
Single 48GB GPU
Optimal for single-model inference, multi-model stacks under 44GB, and fine-tuning up to 13B (FP16) or 34B (QLoRA).
- 14B–35B models at full FP16 — primary sweet spot
- 32B models at INT4 / Q4_K_M
- Multi-model stacks: LLM + TTS + ASR + ComfyUI
- Lowest entry cost — RTX A6000 from $409/mo
Multi-GPU 48GB (NVLink)
Required for 70B+ at full FP16, maximum concurrency, or 70B LoRA fine-tuning.
- 4×A6000 (192GB): 72B at ~449 tok/s — 3× vs 4×A100
- NVLink eliminates PCIe bottleneck
- Recommended for 100+ concurrent users on 70B
1× RTX A6000
48 GB / Single GPU
$409/mo
- 70B Q4_K_M on one card
- 32B at full FP16
- Multi-model stacks <44GB
3× RTX A6000
144 GB
$899/mo / 256GB RAM / 36-Core E5-2697v4
- 100B+ at Q4, 70B at FP16
- 2TB NVMe + 8TB SATA
- 1000Mbps Unmetered
4× RTX A6000
192 GB / 2×NVLink
$1,199/mo / 512GB RAM / 44-Core E5-2699v4
- 72B FP16 — 449 tok/s vLLM
- 4TB NVMe + 16TB SATA
- 1000Mbps Unmetered
When to choose 4×A100 40GB instead
Sub-32B models at high concurrency? The 4×A100 40GB ($1,899/mo, 160GB, 6×NVLink, 512GB RAM) can outperform 4×A6000 due to superior FP16 tensor compute. For 70B+ inference, 4×A6000's 192GB VRAM wins decisively. View multi-GPU configs →
Pricing & Plans
48GB GPU Server Pricing
Flat-rate monthly — no egress fees, no storage surcharges, no cold-start billing. May 2026 — verify at each provider before purchasing.
RTX Pro 5000
Blackwell · GPU VPS · PCIe Passthrough
$299/mo
48 GB GDDR7 · 66.94 TFLOPS FP32
- Dedicated VM via PCIe Passthrough
- Full root SSH, no container limits
- NVMe SSD, flat-rate no egress fees
- 99.9% SLA, <5 min support
- SOC-certified US data center
RTX A6000
Ampere · Bare Metal Dedicated
$409/mo
48 GB GDDR6 ECC · NVLink-capable
- Bare metal — no virtualization layer
- NVLink for dual-GPU 96GB config
- Full root SSH, any CUDA version
- 99.9% SLA, <5 min support
- SOC-certified US data center
Tesla A40
Ampere · Bare Metal Dedicated
$409/mo
48 GB GDDR6 ECC · Data Center Grade
- Bare metal — data center validated
- Passive cooling, dense deployment
- Full root SSH, any CUDA version
- 99.9% SLA, <5 min support
- SOC-certified US data center
Provider Comparison
48GB GPU Server: GPU Mart vs. Alternatives
| Provider | GPU | Monthly | Infrastructure | Egress | SLA | Cold Start |
|---|---|---|---|---|---|---|
| GPU Mart | RTX A6000 / A40 | $409/mo | Bare Metal | None | 99.9% | Always-On |
| GPU Mart | RTX Pro 5000 | $299/mo | GPU VPS PCIe | None | 99.9% | Always-On |
| RunPod | A6000 / A40 (Community) | ~$250–$350 est. | Cloud / Shared HW | None | Best-effort | 30s+ (Serverless) |
| Lambda Labs | RTX A6000 | $784/mo | Cloud Instance | None | Best-effort | Varies |
| AWS (L40S) | L40S | $1,015–$3,261/mo | Cloud VPC | High | 99.9% | Always-On |
RunPod Community Cloud: ~$250–$350/mo — lower nominal price, but third-party hardware with no SLA and 30s+ cold starts on Serverless. Lambda Labs: $784/mo for the same RTX A6000 server — 91% more than GPU Mart, no infrastructure advantage. AWS L40S: up to $3,261/mo for equivalent 48GB VRAM. At 720 hours/month (24/7), GPU Mart's $409 flat rate is 30–50% cheaper than equivalent hourly-billed cloud configurations.
Pricing: provider public pages, May 2026.
Dedicated vs. Cloud
Bare Metal vs. GPU Cloud Instances
Same GPU model, different infrastructure — the gap shows in throughput and reliability at 24/7 utilization.
RTX A6000 Dedicated Server
- No hypervisor — zero compute loss
- Full 48GB VRAM exclusively yours, no time-slicing
- Local NVMe — fast model loading
- NVLink-capable: scale to 96–192GB
- Full root SSH — any CUDA version
- Always-On, no cold starts
- 99.9% SLA, SOC-certified US DC
- Flat-rate $409/mo — predictable billing
A6000 / A40 Cloud Instance
- Virtualization layer: 5–25% compute loss
- Community Cloud: shared hardware, no VRAM exclusivity
- Network storage — slower I/O than local NVMe
- NVLink generally unavailable in cloud configs
- Container environment, limited kernel access
- Serverless: 30s+ cold-start latency
- No hardware SLA on Community tier
- Hourly billing — costly at 24/7 utilization
Why GPU Mart
Why Teams Choose GPU Mart
Self-owned SOC-certified US data centers. Faults credited, never billed.
No hourly swings, no egress fees, no surprise invoices.
Real GPU engineers, 24/7. Direct access to your hardware team.
Database Mart LLC (est. 2005), 7+ years of GPU hosting operations.
Customer Evidence
What Production Teams Say
Running Qwen3.5 35B via vLLM + CosyVoice TTS + PostgreSQL on one RTX Pro 5000. Both models stay in VRAM — no swapping between requests. Consistent throughput, no surprise bills.
GPU Mart Client / vps_119813 / May 2026
6 months in — perfect. Helpful support, good prices, reliable hardware. Recommending!
Verified Customer / gpu-mart.com / 2025
Moved our inference API from a major cloud provider. Consistent throughput, no throttling, no surprise bills.
Verified Customer / gpu-mart.com / 2025
Is This Right For You?
Who Should Use a Dedicated 48GB GPU Server
Best fit
- 14B–35B LLMs at full precision in 24/7 production — the primary sweet spot for 48GB
- Multi-model stacks: LLM + TTS + ASR + image gen
- Flux, SDXL, ComfyUI at full precision
- LoRA / QLoRA fine-tuning — a cost-effective A6000 alternative to cloud training
- 3D rendering scenes over 24GB VRAM
- SOC-compliant on-premises-style LLM hosting
Consider alternatives if…
- Single 7B–13B model at low concurrency — 24GB server ($159/mo) is enough
- Need 100+ concurrent users on 70B — upgrade to 4×A6000 ($1,199/mo)
- Short experiments a few hours/week — hourly billing may cost less
Summary: A dedicated 48GB VRAM GPU server is the right call when your workload runs 24/7, your models are 14B–32B, and you need predictable costs. The RTX Pro 5000 (Blackwell, coming back soon) is the highest-compute 48GB option at $299/mo; the RTX A6000 ($409/mo) is available now with NVLink scaling to 192GB. If you're running short experiments or sub-13B models at low traffic, a smaller option will serve you better — and GPU Mart has those too.
FAQ
Frequently Asked Questions
- What model sizes run best on a 48GB GPU server?
- The sweet spot for 48GB VRAM is 14B–27B models at full FP16 precision — Qwen3 14B, DeepSeek 14B, Gemma 3 12B, and GPT-OSS 20B all load with substantial KV cache headroom remaining. 32B models run well at INT4/Q4_K_M (~18GB). 70B models technically load at heavy quantization (~43–47GB) but leave almost no KV cache space, making production inference impractical at any meaningful concurrency. For 70B at scale, upgrade to 3×A6000 (144GB) or 4×A6000 (192GB, benchmarked at ~449 tok/s for 72B). Source: gpu-mart.com/guides/self-hosted-llm
- What's the difference between the RTX Pro 5000 GPU VPS and the RTX A6000 dedicated server?
- Both have 48GB VRAM, but different architectures and form factors. The RTX Pro 5000 (Blackwell, 66.94 TFLOPS, 576 GB/s, GDDR7) is delivered as a GPU VPS via PCIe Passthrough — higher single-GPU compute, ideal for small-to-mid model inference. The RTX A6000 (Ampere, 38.71 TFLOPS, 768 GB/s, GDDR6 ECC) is bare metal dedicated — no virtualization layer, NVLink-capable for multi-GPU 96–192GB configs. Choose Pro 5000 for max per-card throughput; choose A6000 if NVLink multi-GPU scaling is on your roadmap.
- Why is 4×A6000 faster than 4×A100 40GB for 72B models?
- A 72B model (~137GB) leaves almost no KV cache room in 4×A100's 160GB total. 4×A6000's 192GB provides ~55GB of KV cache headroom — producing ~449 tok/s vs ~154 tok/s (~3× throughput) from VRAM headroom alone, not faster compute. Source: databasemart.com/blog/vllm-gpu-benchmark-a100-40gb-4
- How does GPU Mart compare to RunPod and Lambda Labs?
- GPU Mart's RTX A6000 and A40 dedicated servers ($409/mo) are dedicated physical hardware — no virtualization, no shared resources, 99.9% SLA. Pro 5000 ($299/mo) VPS also has dedicated GPU card. RunPod Community Cloud uses third-party hardware with no hardware SLA; RunPod Serverless has 30s+ cold starts unsuitable for real-time inference. Lambda Labs charges $784/mo for the same A6000 config — 91% more — making GPU Mart the most cost-effective dedicated A6000 alternative for production inference.
- Can I run multiple models simultaneously on a 48GB server?
- Yes — as long as total resident VRAM stays below ~44–45GB. Real client examples on RTX Pro 5000 and A6000: Qwen 35B (28GB) + CosyVoice TTS (11GB); Qwen3 35B (28GB) + ComfyUI (18GB) + Whisper; Qwen3-8B + Gemma3-12B permanently resident for dual-LLM inference.
- What SLA applies and what if hardware fails?
- 99.9% uptime SLA — ~43 min max downtime/month. GPU Mart owns the hardware in SOC-certified US data centers, responds in under 5 minutes, and handles remediation directly. Fault periods are credited, not billed.
- What's the hidden cost trap with hourly GPU cloud billing?
- At 24/7 utilization (720 hours/month), hourly GPU cloud billing at $0.80–$1.20/hr converts to $576–$864/mo — well above GPU Mart's $409/mo flat rate for A6000. Inference APIs run continuously; model-warm time and queued requests accumulate billing hours. Flat-rate pricing eliminates this unpredictability. The break-even vs. hourly billing is typically around 300–400 hours/month of actual GPU usage.
- Is bandwidth included in the flat-rate price?
- Yes — 100-1000Mbps unmetered bandwidth, no egress fees, unlike AWS/GCP which charge per-GB. Verify the latest policy at gpu-mart.com before ordering if high-throughput egress is critical.
Deploy Your 48GB GPU Server Today
Pro 5000/ RTX A6000 / A40 / From $269/mo flat rate / 99.9% SLA / No cold starts