---
license: mit
task_categories:
  - text-generation
tags:
  - benchmark
  - inference
  - llm
  - nvidia
  - rtx-5090
  - llama-cpp
  - vllm
  - speed
  - moe
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: benchmarks.csv
---

# RTX 5090 LLM Benchmarks

Speed benchmarks for quantized LLMs on NVIDIA RTX 5090 32GB, measured with [llm-bench-rig](https://github.com/notwitcheer/llm-bench-rig).

## What's measured

- **Prompt processing (pp)**: parallel batched token throughput at context lengths 128, 512, 2048, 4096, 8192, 16384
- **Text generation (tg)**: sequential autoregressive token throughput at 128 tokens
- All models fully GPU-offloaded (ngl=99)

## Hardware

| Component | Spec |
|---|---|
| GPU | NVIDIA GeForce RTX 5090 32GB (Blackwell, sm_120a) |
| CPU | AMD Ryzen 5 9600 (6c/12t) |
| RAM | 64GB DDR5-5600 |
| OS | Ubuntu 26.04 LTS |
| CUDA | 12.8 (patched for glibc 2.41) |

## Schema

| Column | Description |
|---|---|
| `model` | Model name |
| `architecture` | Dense or MoE (with active param count) |
| `params_b` | Total parameters in billions |
| `quant` | Quantization method |
| `size_gib` | File size in GiB |
| `engine` | Inference engine (llama.cpp or vLLM) |
| `backend` | Compute backend (CUDA) |
| `gpu` | GPU model |
| `vram_gb` | VRAM in GB |
| `test` | Benchmark test (pp128, pp512, ..., tg128) |
| `tokens_per_sec` | Throughput in tokens/second |
| `stddev` | Standard deviation |
| `date` | Benchmark date |

## Key findings

MoE (3B active) vs Dense (27B) on same-family Qwen3.6 models:
- Prompt processing: **2.4x faster** across all context lengths
- Text generation: **3.5x faster** (271 vs 77 t/s)
- Both degrade ~17% at 16K context (attention + VRAM, not parameter count)

## Tooling

Benchmarks generated with [llm-bench-rig](https://github.com/notwitcheer/llm-bench-rig) — open-source benchmark pipeline for GGUF and safetensors models.