4× RTX 3090 vs Fewer High-End GPUs

1. The GPU Quantity vs Quality Debate

When building a local AI inference rig, you face a fundamental choice: go wide with many cheaper GPUs, or go tall with fewer high-end cards. Our $5K GPU rig build uses 4× RTX 3090 — giving a staggering 96GB of total VRAM for around $3,000 in GPUs alone. But is that actually the best strategy?

What if you spent the same ~$3,000-3,500 on:

• 2× RTX 4090 — newer, faster architecture but only 48GB total?
• 1× RTX 5090 — the newest Blackwell consumer card with 32GB?
• 1× A100 80GB — a data center GPU with HBM2e memory?

This article digs into the real benchmarks, real prices, and real tradeoffs. The answer isn't simple — it depends entirely on what models you want to run and how fast you need them.

2. The Contenders

Here are the GPU configurations we're comparing, all within roughly the same $3,000-3,500 GPU budget:

3. VRAM Analysis — Why It Matters Most

For LLM inference, VRAM is the single most important metric. If a model doesn't fit in your GPU memory, you can't run it at full speed — period. You'd have to offload layers to system RAM (10-50× slower) or use more aggressive quantization (lower quality).

How much VRAM do popular models need?

This is where the 4× RTX 3090 strategy shines. With 96GB of total VRAM, you can run Llama 70B in FP16 (with some overhead for KV cache), Mixtral 8×7B comfortably, and even Llama 405B in aggressive Q2/Q3 quantization. No other configuration in our budget comes close.

4. Performance Benchmarks

We aggregated benchmarks from llama.cpp (the gold standard for local LLM inference testing) across multiple community sources. All numbers are token generation speed in tokens/second — what determines how fast text appears on screen.

Llama 3 8B (Q4_K_M) — Small Model Speed

Key insight: For small models that fit on a single GPU, more GPUs actually makes things slower due to communication overhead. A single RTX 5090 at 186 t/s crushes 4× RTX 3090 at 105 t/s.

Llama 3 70B (Q4_K_M) — The Big Model Test

5. The Multi-GPU Tax

When a model is too large for a single GPU, you split it across multiple cards using tensor parallelism (TP). Each GPU handles a portion of the model, but they need to constantly exchange intermediate results. This communication has a cost.

PCIe vs NVLink

The RTX 3090 does not have NVLink. Neither does the RTX 4090 or RTX 5090. All consumer GPUs communicate over PCIe, which is ~20× slower than NVLink. This is why splitting a model across 4× RTX 3090 over PCIe is significantly slower than running it on a single A100 with no communication needed at all.

Real-world overhead measurements

• 2-way TP over PCIe: ~10-15% throughput loss vs single GPU
• 4-way TP over PCIe: ~20-35% throughput loss vs single GPU
• 8-way TP over PCIe: ~40-50% throughput loss vs single GPU
• 2-way TP over NVLink: ~3-5% throughput loss

For our 4× RTX 3090 running 70B Q4 across all four cards, we see about ~17 t/s. A hypothetical single GPU with 96GB VRAM and equivalent compute would likely achieve 25+ t/s. That's the multi-GPU tax in action.

6. Power & Cooling

Annual cost assumes $0.12/kWh US average and 24/7 operation at 80% load.

The 4× RTX 3090 setup is the most power-hungry option — 1,400W of GPU power alone requires a dedicated 20A circuit and proper cooling. The A100 is by far the most efficient at 300W for 80GB of VRAM, and the RTX 5090 is impressively efficient at 575W.

7. Price per GB of VRAM

This is the efficiency metric that matters most for budget builders:

The RTX 3090 at $31.25 per GB is unbeatable. The RTX 4090 costs $75/GB — more than double — for the same 24GB per card. You're paying for the newer architecture and faster compute, not for more memory.

8. Model Compatibility Matrix

The 4× RTX 3090 can run the widest range of models. The RTX 5090, despite being the newest and fastest card, is limited by its 32GB VRAM — it can't even run Llama 70B quantized. The A100 80GB sits in a sweet spot for single-GPU simplicity on large models, but at 5-6× the cost.

9. The Single-GPU Advantage

Running on a single GPU has real benefits:

• Zero communication overhead — no tensor parallelism means no PCIe bottleneck
• Simpler setup — no multi-GPU configuration, no breakout boards, no risers
• Lower power draw — one card instead of four
• Lower latency — time-to-first-token is better without TP synchronization
• Easier debugging — CUDA errors are simpler to diagnose on one device

The A100 80GB is the ultimate single-GPU option: 80GB of HBM2e with 2,039 GB/s bandwidth means Llama 70B Q4 fits entirely on one card and runs at 22 t/s with zero overhead. The RTX 5090 with 32GB is blazingly fast for models up to ~30B parameters — hitting 186 t/s on 8B models.

If you primarily run models ≤32GB, a single RTX 5090 is genuinely the best choice. It's faster than 4× RTX 3090 for those models, uses less power, and costs less.

10. The Multi-GPU Advantage

But running multiple GPUs has its own compelling benefits:

• Total VRAM scales linearly — 4× 24GB = 96GB, 8× 24GB = 192GB
• Run multiple models simultaneously — dedicate 1 GPU to Llama 8B for chat, another to SDXL for images
• Batch inference throughput — serve multiple requests in parallel across GPUs
• Redundancy — if one GPU fails, the others keep working
• Gradual scaling — start with 2, add 2 more later, eventually go to 8
• Future-proofing — as models get bigger, you already have the VRAM

The 4× RTX 3090 setup is uniquely flexible. You can run 4 separate small models simultaneously (one per GPU), or combine all 4 for a single massive model. Try doing that with a single RTX 5090.

11. The Verdict: When to Go Wide vs Tall

Choose 4× RTX 3090 (go wide) when:

• You want to run 70B+ parameter models locally
• VRAM capacity is your #1 priority
• You want the best price per GB of VRAM ($31/GB)
• You need flexibility to run multiple models simultaneously
• You plan to expand to 6-8 GPUs later
• You want to experiment with the largest open-source models

Choose 2× RTX 4090 (go tall) when:

• You primarily run models that fit in 48GB (most quantized 70B models)
• Tokens/second speed matters more than model variety
• You want lower power consumption (900W vs 1,400W)
• You need newer architecture features (AV1 encoding, DLSS 3)

Choose 1× RTX 5090 (go minimal) when:

• You primarily run models ≤32B parameters
• Raw speed is paramount (186 t/s on 8B models!)
• Simplicity and low power draw matter most
• You don't need 70B models

Choose 1× A100 80GB (go pro) when:

• Budget is not a constraint ($8,000+)
• You want 70B models on a single GPU with no overhead
• HBM2e bandwidth matters for your workload
• You're running a production inference service 24/7

12. Our Build Guides

Ready to build? Check out our complete build series:

• 📋 $5K GPU Rig — Complete Shopping List — Every component with Amazon buy links. Start with 4× RTX 3090.
• 🏗️ Pro Tier GPU Rig — Server-grade build with ASRock Rack ROMED8-2T, EPYC CPU, full PCIe 4.0 bandwidth.
• ⚖️ Budget vs Pro Tier — Side-by-side comparison to help you decide which build is right.
• 🔧 DIY vs Off-the-Shelf — Our build vs Mac Studio, Jetson AGX Orin, HP Z4, and more.
• 📖 Multi-GPU Software Setup Guide — Ubuntu, CUDA, vLLM, llama.cpp — the complete walkthrough.

This article was written collaboratively by Michel (human) and Yaneth (AI agent) as part of ThinkSmart.Life's research initiative. Prices reflect February 2026 market conditions and may fluctuate — always check current listings before purchasing.