How to Optimize LLM Inference Speed on RTX 5090: The 2026 Performance Guide

Quick Summary: Key Takeaways

The 32GB Ceiling: The RTX 5090’s 32GB VRAM is massive, but unquantized 70B models will still choke it; smart quantization is mandatory.
TensorRT-LLM is King: Moving from standard Python loaders to NVIDIA’s TensorRT-LLM can double your tokens-per-second (TPS).
Driver Hygiene: You must run the latest Game Ready or Studio Drivers (570.xx series or higher) to unlock native FP8 support.
Quantization Sweet Spot: For coding and reasoning models like DeepSeek R1, 4-bit (AWQ) offers the best balance of speed vs. accuracy.
System RAM Matters: Ensure you have at least 64GB of system RAM to handle offloading during model loading times.

Unlocking the Beast: Your RTX 5090 is Underperforming

If you just plugged in your new card and started generating text, you are likely leaving 40% of your performance on the table. Learning how to optimize LLM inference speed on RTX 5090 is about more than just raw power; it is about software pipeline efficiency.

In 2026, the RTX 5090 is the undisputed king of consumer AI, but "out of the box" settings are rarely optimized for Large Language Models (LLMs). This deep dive is part of our extensive guide on LMSYS Chatbot Arena Leaderboard Current: Why the AI King Just Got Dethroned.

Whether you are running local agents or testing the latest open-weights models, every millisecond of latency counts.

Step 1: The Foundation (Drivers & CUDA)

Before touching any model weights, your environment needs to be bulletproof. The RTX 5090 introduces native hardware acceleration for specific FP8 data types that older cards lacked.

Essential Setup:

Drivers: Update to NVIDIA Driver 570.xx or later. Do not rely on Windows Update; download directly from NVIDIA.
CUDA Toolkit: Ensure you are running CUDA 13.x, which includes the latest kernels for Blackwell architecture.

If you are trying to run these models on a laptop instead of a desktop rig, the thermal constraints change the math entirely. See our hardware reality check in the Best Laptops for Running Local LLMs 2026 guide.

Step 2: TensorRT-LLM (The Speed Hack)

For years, developers relied on generic PyTorch loaders. In 2026, if you aren't using TensorRT-LLM, you are running in slow motion. TensorRT compiles your LLM specifically for your GPU's architecture. It fuses operations and optimizes memory access patterns.

The Performance Gap:

Standard Loader: ~85 tokens/sec (Llama 3 8B)
TensorRT-LLM: ~140+ tokens/sec (Llama 3 8B)

How to Enable It: Most modern backends like Triton Inference Server or specialized forks of text-generation-webui now have one-click installers for TensorRT engines. Use them.

Step 3: Quantization Strategy (The VRAM Trap)

The RTX 5090 boasts 32GB of VRAM. This sounds like a lot, but a full precision (FP16) 70B parameter model requires roughly 140GB of VRAM. To fit massive intelligence into a consumer card, you must quantize.

The Golden Rules for RTX 5090:

8-Bit (FP8): Use this for models under 30B parameters. It retains near-perfect accuracy.
4-Bit (AWQ/EXL2): This is mandatory for 70B models (like Llama 3.1 70B).

Does it hurt intelligence? According to recent benchmarks, modern 4-bit quantization degrades coding performance by less than 1.5% while reducing VRAM usage by 50%.

Step 4: Memory Management (KV Cache)

Long-context windows (128k+) are the killer of inference speed. As your conversation grows, the "KV Cache" (Key-Value Cache) eats up your VRAM rapidly.

Optimization Tips:

PagedAttention: Ensure your inference backend uses PagedAttention (popularized by vLLM). It manages memory like an OS manages RAM, preventing fragmentation.
Context Limits: Hard-cap your context window to what you actually need. Don't allocate 128k tokens if you only need 8k.

Conclusion

The RTX 5090 is a marvel of engineering, but it requires a skilled pilot. By utilizing TensorRT-LLM, mastering 4-bit quantization, and managing your KV cache, you can transform your PC into an enterprise-grade AI server.

Knowing how to optimize LLM inference speed on RTX 5090 is the difference between a sluggish chatbot and a real-time intelligence engine.

Frequently Asked Questions (FAQ)

1. Can the RTX 5090 run unquantized Llama 3.1 70B?

No. An unquantized (FP16) 70B model requires approx. 140GB of VRAM. The RTX 5090 has 32GB. You must use 4-bit quantization (EXL2 or AWQ) to fit it entirely on the GPU, or split it across multiple GPUs.

2. What is the best quantization level for DeepSeek R1 on RTX 5090?

For DeepSeek R1, 4-bit quantization (specifically AWQ) is the sweet spot. It allows the model to fit comfortably within the 32GB VRAM buffer while leaving room for the context window (KV Cache), with negligible loss in reasoning capabilities.

3. How to use TensorRT-LLM for faster inference?

You can use TensorRT-LLM by compiling your specific model weights into a "TensorRT Engine." The easiest method for beginners is to use a backend wrapper like "TensorRT-LLM Backend" for Triton or use compatible loaders in text-generation-webui that support .engine files.

4. How does batch size affect LLM throughput on 32GB VRAM?

Increasing batch size improves total throughput (tokens processed per second) but increases latency for individual responses. On 32GB VRAM, keep batch sizes small (1-4) for real-time chat to minimize VRAM usage for the KV cache.

5. How to offload LLM layers to the GPU in Llama.cpp?

In Llama.cpp, use the -ngl (number of GPU layers) flag. For the RTX 5090, set this to the maximum number of layers your model possesses (e.g., -ngl 33 for a 7B model) to ensure the entire model runs on VRAM, maximizing speed.