Hardware → models
RTX 4090 24 GB
Start with gpt-oss-20b at MXFP4 profile for general chat. For a coding-focused option, try Qwen3-Coder 30B-A3B Instruct.
Models to start with
Planning estimates24 GB dedicated VRAM · 4,000 tokens · 32 GB system RAM · full GPU residency
| Model / starting file | Why consider it | GPU memory estimate | Memory left¹ |
|---|---|---|---|
| gpt-oss-20bMXFP4 profile | Reasoning and tool-oriented tasksNeeds a runtime that supports MXFP4 and the Harmony conversation format. | 14.8 GB | 8.5 GB |
| Qwen3-Coder 30B-A3B InstructQ4_K_M | Coding mixture-of-experts modelFull weights must fit despite 3.3B active parameters. Use a compatible runtime. | 20.8 GB | 2.5 GB |
| Llama 3.2 3B InstructQ4_K_M | Small text assistantA smaller weight footprint. Check the exact Llama checkpoint and licence. | 3.7 GB | 19.6 GB |
From the published model guides here; the Finder covers the full catalogue. ¹ Memory left after the Finder’s reserve. Other applications can use this space. Larger models and higher precision do not guarantee better answers.
24 GB allows more context or a larger quantisation. It does not establish speed or output quality. CPU offloading changes the memory allocation and may slow generation.
Setup and common questions
Which RTX 4090 does this page cover?
The desktop RTX 4090 has 24 GB GDDR6X. The RTX 4090 Laptop GPU is a separate product. Check the exact manufacturer’s dimensions, connectors and power requirements before installing a card.
What should I check on my first run?
Use a working NVIDIA backend and one model at 4K context. Confirm GPU offload and watch memory during generation. Keep the file, prompt and thinking mode fixed between runs.
Can 24 GB handle maximum context?
Not necessarily. Cache memory grows with context and simultaneous conversations. A model that fits at 4K can exceed this budget at its advertised maximum. See context length and test the settings your workload needs.
How are these recommendations chosen?
We use the main Finder’s balanced task selection and memory calculation, limited to models with published guides here. Task scores and speed proxies are planning inputs, not measured quality rankings.
GPU memory includes weights, context cache and a 0.9 GB runtime allowance, plus a 3% reserve (0.15–1.5 GB). Cache format and runtime buffers can change the fit. See the calculation assumptions.
Sources and related guides
Specification sources checked 2 October 2026.
These comparisons are planning estimates. No reviewed or independently reproduced measurements are available yet. About test evidence.