The RTX 5090 starts at $0.86/hr on Spheron. The RTX 4090 starts at $0.53/hr. That $0.33/hr gap is significant. What makes the comparison interesting is the 78% memory bandwidth difference (1,792 vs 1,008 GB/s) and 8GB more VRAM. Whether those specs justify the premium depends entirely on what model you're running and at what throughput.

For Llama 3.1 8B in FP16, the RTX 5090 delivers 3,500 tok/s vs 2,550 tok/s on the RTX 4090. But with current on-demand rates, the RTX 4090 costs $0.058/M tokens vs $0.068/M for the RTX 5090. The 4090 is both slower and cheaper per token for small models that fit in 24GB. The 5090 wins on throughput and on larger models. This post gives you the numbers to decide.

Quick Answer: RTX 5090 vs RTX 4090 for AI

Prices as of 03 May 2026. Check current GPU pricing for live rates.

Full Spec Comparison

On the GDDR7 vs HBM distinction: Both the RTX 5090 and RTX 4090 use GDDR memory, not HBM. The RTX 5090 uses GDDR7, which has a significant bandwidth improvement over GDDR6X, but it is still categorically different from the HBM2e/HBM3 used in the H100. The RTX 5090's 1,792 GB/s is impressive for GDDR but sits at roughly 54% of the H100 SXM5's 3,350 GB/s HBM3 bandwidth. This matters for very large batch workloads where HBM bandwidth compounds.

Which Models Actually Fit

RTX 5090: 32GB GDDR7

RTX 4090: 24GB GDDR6X

On Qwen3 32B on the RTX 4090: The model weights at Q4/AWQ are roughly 18-20GB, which fits in 24GB. The problem is the KV cache. At default context lengths in vLLM (typically 4K-32K tokens), the KV cache adds several GB on top of model weights, pushing total VRAM usage over 24GB. The fix is to set --max-model-len 2048 in vLLM, which limits the KV cache footprint. This works for short-context use cases but is not practical for production serving at standard context lengths. For the full model capacity matrix, see GPU memory requirements for LLMs. For a detailed walkthrough of AWQ quantization and how to deploy Qwen3 32B in production, see our AWQ quantization guide for LLM deployment.

Inference Benchmarks: vLLM Performance

For the best vLLM configuration on consumer GPUs, see our vLLM production deployment guide for recommended serving flags and batch size tuning.

RTX 5090 throughput from community vLLM runs and Spheron internal testing. RTX 4090 throughput from published llama.cpp and vLLM benchmarks. Cost calculated at on-demand pricing as of 03 May 2026. †FLUX.1 Dev on RTX 4090 requires memory-efficient attention (enable_xformers_memory_efficient_attention() or SDPA backend in diffusers); default pipeline settings may OOM.

For Llama 3.1 8B, the RTX 4090 at $0.058/M tokens is about 15% cheaper per token than the RTX 5090 at $0.068/M. The RTX 5090's higher throughput does not offset its higher hourly rate for models that fit in 24GB. The bandwidth advantage of the RTX 5090 becomes economically relevant when you move to 13B+ FP16 models or need the extra VRAM headroom for Qwen3 32B. For sub-7B models at INT4 quantization, both cards are largely bandwidth-saturated at small batch sizes, and the RTX 4090's lower rate wins outright.

FP4 note: FP4 support in vLLM for RTX 5090 is currently in preview. Benchmark numbers for FP4 workloads assume --quantization fp4 and a Blackwell-compatible vLLM build. Check vLLM release notes for stable support status before relying on FP4 in production. For performance benchmarks and the quantization workflow for FP4 on Blackwell, see FP4 quantization on Blackwell GPUs.

Fine-Tuning Benchmarks: QLoRA Throughput

For a complete walkthrough of QLoRA setup, hyperparameters, and dataset preparation, see our complete LLM fine-tuning guide.

The RTX 5090's 32GB headroom makes a real difference for fine-tuning: you can run Llama 3.1 13B at FP16 precision with LoRA adapters without hitting VRAM limits, whereas the RTX 4090 needs INT4 quantization to fit. The ~38% throughput improvement (720 vs 520 tok/s for 8B QLoRA) is consistent with the bandwidth-bound nature of QLoRA, though the full 78% bandwidth advantage does not translate directly to throughput due to compute and memory-copy overhead during the backward pass.

Cost Per Million Tokens: The Real Math

Using live Spheron on-demand pricing as of 03 May 2026:

Formula: Cost/M tokens = (hourly rate) / (tokens per second x 3600) x 1,000,000

The RTX 4090 at $0.53/hr wins on cost-per-token for FP16 workloads that fit in 24GB: $0.058/M tokens vs $0.068/M for Llama 3.1 8B. The RTX 5090 wins on raw throughput (35-46% more tok/s) and becomes the only practical option for 13B+ FP16 models and Qwen3 32B AWQ at standard context lengths. If your budget is fixed and your model fits in 24GB, the RTX 4090 delivers better value per token. If you need maximum throughput or larger VRAM headroom, the RTX 5090 is worth the $0.33/hr premium. For a broader benchmark across more GPU models and workload types, see GPU cost-per-token benchmarks for LLM inference 2026.

Pricing fluctuates based on GPU availability. The prices above are based on 03 May 2026 and may have changed. Check current GPU pricing → for live rates.

When the RTX 5090 Wins

• 13B-32B parameter models: The extra 8GB VRAM moves you from "marginal" to "comfortable" for models in this range. Llama 3.3 13B fits at FP16. Qwen3 32B at AWQ fits with room for KV cache.
• FP4 workloads (Blackwell-native): Only Blackwell GPUs support FP4. When tooling matures, FP4 will deliver roughly 2x throughput over FP8 on the same GPU. The RTX 4090 cannot participate in FP4 inference at all.
• High-volume inference on 13B+ models: The RTX 5090 is the only single-GPU option for Llama 3.3 13B at FP16 or Qwen3 32B at AWQ with practical context lengths. For models that fit only on the 5090, there is no cost comparison to make.
• QLoRA fine-tuning up to 30B: The 32GB VRAM lets you run 13B QLoRA at FP16 precision. The 4090 requires INT4 for anything beyond 8B, adding quantization overhead and reducing gradient quality.
• FLUX and diffusion at high throughput: 5.5 img/min vs 4.0 img/min is a 38% throughput difference. The RTX 4090 result requires memory-efficient attention (xFormers/SDPA) to stay within 24GB; the default diffusers pipeline may OOM without it. If your constraint is turnaround time rather than cost-per-image, the RTX 5090 finishes batch jobs significantly faster and runs FLUX.1 Dev BF16 without any memory workarounds.

Start your work on an RTX 5090 GPU rental on Spheron with per-minute billing and no minimum commitment.

When the RTX 4090 Still Wins

• Lowest absolute cost for sporadic small-model inference: If you're running sub-7B models at low concurrency with significant idle time, the $0.33/hr savings and lower cost-per-token at INT4 favor the 4090. At batch size 1 with intermittent requests, GPU utilization is low on both cards and the absolute hourly savings matter more than throughput.
• Ada Lovelace driver maturity: The RTX 4090 has been in data centers and developer machines for two years. The driver stack, CUDA toolkit compatibility, and software ecosystem around Ada Lovelace are more tested than early Blackwell consumer deployments. If you're seeing edge-case driver issues on RTX 5090, the 4090 is more predictable.
• Local buy vs rent analysis: At an MSRP of ~$1,599 for the RTX 4090 vs $2,000+ for the RTX 5090, the on-prem cost differential is meaningful for permanent workstations. The cloud rental gap at $0.33/hr is also significant, though the RTX 4090's lower cost-per-token for small models makes it attractive in cloud contexts too.
• Development and prototyping at low utilization: If you're iterating on prompts, testing fine-tuned model outputs, or exploring a new architecture, you don't need 3,500 tok/s. You need 500 tok/s and a quick feedback loop. The 4090 is perfectly capable and $0.33/hr cheaper.

Book an RTX 4090 GPU rental on Spheron for development and low-volume inference.

Decision Framework: Which Card for Your Use Case

When to Skip Both: L40S, A100, and H100

L40S (48GB GDDR6): If your model is in the 30B-70B range at INT4, the L40S provides 48GB of VRAM for ~$0.72/hr. This is more VRAM than the RTX 5090 at a similar or lower price point for many workloads. The L40S is also NVIDIA's data center GPU line, so it avoids the GeForce EULA restrictions that technically prohibit consumer GPU use in commercial data center deployments. For detailed vLLM benchmarks on L40S, see NVIDIA L40S for AI inference. Rent L40S on Spheron.

A100 80GB (HBM2e): The A100 80GB provides 80GB of HBM2e memory and NVLink connectivity, making it the right choice for 70B parameter inference at FP16 or large-batch INT4 workloads where HBM bandwidth matters. On Spheron, A100 instances start at $0.45/hr spot. The memory subsystem is fundamentally different from consumer GDDR: HBM delivers higher total bandwidth for large model serving and enables true multi-GPU tensor parallelism via NVLink. Rent A100 on Spheron.

H100 (HBM2e/HBM3): For 70B+ models at production scale, ECC memory requirements, or multi-GPU NVLink tensor parallelism, the H100 is the correct choice. The PCIe variant at $2.01/hr handles 70B FP8 inference on a single GPU. For a detailed comparison of the RTX 5090 against the H100 and B200, see our RTX 5090 vs H100 vs B200 guide. If you're deciding between a consumer GPU and renting H100 time in the cloud, the H100 vs RTX 4090 comparison breaks down the full economics including cost-per-million-token math and the hybrid 4090-dev/H100-train workflow. Rent H100 on Spheron.

RTX PRO 6000 (96GB GDDR7): If you need more than 32GB on a single Blackwell card, the RTX PRO 6000 on Spheron is the only option in this tier. With 96GB of GDDR7 and ECC memory, it runs 70B FP8 and 32B FP16 models that cannot fit on the RTX 5090 or RTX 4090. For a direct two-way comparison of the RTX 5090 against the RTX PRO 6000 Blackwell, see RTX 5090 vs RTX PRO 6000 for AI (2026).

Both cards are available on Spheron with bare-metal access, per-minute billing, and no contracts. Compare live on-demand and spot rates, then deploy in minutes.

Rent RTX 5090 → | Rent RTX 4090 → | View all GPU pricing →