Model × hardware check
Can gpt-oss-20b run on NVIDIA GeForce RTX 4090 · 24 GB?
Yes, this is a comfortable estimate at 8,192 tokens. a compact MoE package for local coding and agent experiments The RTX 4090 offers 24 GB of VRAM and 1008 GB/s of memory bandwidth, but it still cannot turn an oversized model into a comfortable fit.
Short answer
Comfortable fit
At least 20% memory headroom remains after weights, KV cache, and runtime overhead.
- Available VRAM
- 24 GB
- Estimated requirement
- 13.8 GB
- Headroom
- +10.2 GB
- Fit ratio
- 174%
- Recommended quantization
- MXFP4
- Context ceiling
- 131,072 tokens
- Speed estimate
- Estimate · Very fast relative band
What the numbers say
The selected gpt-oss-20b package uses 12.1 GB of weight files. The 8K KV cache estimate is 0.2 GB, and the formula adds 1.5 GB of runtime overhead. That produces a 13.8 GB requirement against 24 GB of VRAM.
24 GB of VRAM can cover several quantized 30B-class models; larger models still need a lower-bit package, CPU offload, or more accelerators.
The context ceiling shown above is the point where the same formula reaches the machine's capacity. It is not a promise that the backend can allocate that full context after the operating system and other applications take their share.
Quantization trade-offs at 8K
| Quantization | Weight files | 8K requirement | Fit on this machine | Context ceiling | Speed estimate | Ollama |
|---|---|---|---|---|---|---|
| MXFP4 | 12.11 GB | 13.8 GB | Comfortable fit · 174% | 131,072 tokens | Estimate · Fast relative class | ollama run gpt-oss:20b |
For MoE models, a tight CPU-offload result means the weights fit in system memory even when they do not fit in accelerator memory. That does not increase the accelerator context ceiling.
Run commands
ollama run gpt-oss:20blms get "gpt-oss-20b"@mxfp4
lms load <model-key-from-lms-ls> --gpu max --context-length 8192llama-cli -hf openai/gpt-oss-20b -c 8192 -ngl 99 -p "Your prompt"Commands are examples, not a guarantee that the selected quantization tag is available in every runtime. Confirm the package name and license on the linked model page.
If it does not fit
This pairing already clears the comfort threshold
The current estimate leaves +10.2 GB. A longer context or another GPU application can reduce that margin, so verify free memory before relying on it.
Low-cost setup upgrades
Useful upgrades around NVIDIA GeForce RTX 4090 · 24 GB
These are optional setup improvements, not a claim that an accessory can increase GPU or unified-memory capacity.
External NVMe SSD for model storage
Keeps large GGUF and MLX files off the boot drive. Fast storage reduces load time, but it does not increase the memory available for inference.
Search current listings on AmazonDesktop RAM upgrade kit
Useful for MoE CPU offload and keeping the operating system out of VRAM. Match the motherboard's memory type, slot layout, capacity limit, and validated part numbers.
Search current listings on AmazonGPU support bracket
A lower-cost mechanical upgrade for heavy three-slot cards. Check clearance, mounting height, and whether the bracket blocks fans or case panels.
Search current listings on AmazonAs an Amazon Associate, LocalLM Fit earns from qualifying purchases. Links are sponsored search links and do not change the price you pay. Affiliate disclosure.
Where this estimate can break
- The Harmony response format is required; using an unrelated chat format can produce incorrect output.
- OpenAI's 16 GB guidance is for the MXFP4 build and does not guarantee room for a long context.
- Other GPU applications, display buffers, and the operating system reduce usable memory.
- Longer prompts, concurrent requests, and a different backend can raise peak memory.
Compare this pairing
FAQ
Questions about this pairing.
How much memory does gpt-oss-20b need on NVIDIA GeForce RTX 4090 · 24 GB?
At an 8K context with MXFP4, the estimate is 13.8 GB: 12.1 GB of weights, 0.2 GB of KV cache, and 1.5 GB of runtime overhead. The available VRAM is 24 GB.
What context length can NVIDIA GeForce RTX 4090 · 24 GB handle with this model?
The calculated ceiling is 131,072 tokens for this quantization, capped by the model's native 131,072-token context. Actual usable context can be lower after the OS, other applications, and backend workspace.
How does quantization change the memory requirement on NVIDIA GeForce RTX 4090 · 24 GB?
At 8,192 tokens, the published variants calculate as follows: MXFP4: 13.8 GB, comfortable fit. A lower-bit file can move a result from too large to tight, but quality and kernel support may differ.
What can I run if gpt-oss-20b does not fit on NVIDIA GeForce RTX 4090 · 24 GB?
This selection already clears the comfort threshold. If you later increase the context, monitor the ceiling of 131,072 tokens; smaller alternatives include Qwen3.8 27B, Gemma 4 12B Unified, Qwen3 8B.
Is the speed estimate for gpt-oss-20b on NVIDIA GeForce RTX 4090 · 24 GB measured?
No. Very fast relative band is a heuristic band derived from 3.6B active parameters and 1008 GB/s published memory bandwidth. Backend kernels, batch size, thermals, and CPU offload can change actual tokens per second.