GPT-OSS 20B needs ~18.5 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~54 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Tight fit
Decode
53.9 tok/s
TTFT
3591 ms
Safe context
26K
Memory
18.5 GB / 20.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Tight fit | 53.9 tok/s | 1959 ms | 26K |
| Coding | S | Tight fit | 53.9 tok/s | 3591 ms | 26K |
| Agentic Coding | S | Runs with offload (needs ~0.5 GB host RAM) | 36.9 tok/s | 7636 ms | 26K |
| Reasoning | S | Tight fit | 53.9 tok/s | 4244 ms | 26K |
| RAG | S | Runs with offload (needs ~0.5 GB host RAM) | 36.9 tok/s | 9545 ms |
How GPT-OSS 20B (21B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.2 GB | Low | S88 |
Q3_K_S | 3 | 10.3 GB | Low | S89 |
NVFP4 | 4 |
Copy-paste commands to run GPT-OSS 20B on your machine.
Run
ollama run gpt-ossYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 30.5B | A | 23.2 tok/s | ||
| 27B | A | 10.4 tok/s |
Yes, RTX 4000 Ada 20GB can run GPT-OSS 20B with a S grade (Tight fit). Expected decode speed: 53.9 tok/s.
GPT-OSS 20B (21B parameters) requires approximately 18.5 GB of memory with Q4_K_M quantization.
The recommended quantization for GPT-OSS 20B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4000 Ada 20GB, GPT-OSS 20B achieves approximately 53.9 tokens per second decode speed with a time-to-first-token of 3591ms using Q4_K_M quantization.
For coding workloads, GPT-OSS 20B on RTX 4000 Ada 20GB receives a S grade with 53.9 tok/s and 26K context.
On RTX 4000 Ada 20GB, GPT-OSS 20B can safely use up to 26K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gpt-oss-20b-on-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 26K |
| Medium |
| S89 |
Q4_K_M | 4 | 12.8 GB | Medium | S89 |
Q5_K_MBest for your GPU | 5 | 15.1 GB | High | S88 |
Q6_K | 6 | 17.2 GB | High | F0 |
Q8_0 | 8 | 22.5 GB | Very High | F0 |
F16 | 16 | 43.1 GB | Maximum | F0 |
| 27B | S | 13 tok/s |
| 30B | A | 24.6 tok/s |
| 24B | S | 15 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.