GPT-OSS 20B CLOUD VRAM Requirement and Ollama Model Size
GPT-OSS 20B CLOUD needs 20GB minimum VRAM, 28GB optimal VRAM, and 64GB+ system RAM for a safer Ollama run. Popular Ollama model family: GPT-OSS. Caveat: Estimated values are placeholders unless marked measured..
Hardware Snapshot
| Family | GPT-OSS |
|---|---|
| Scenario | reasoning |
| License scope | open-weight |
| Quantization | CLOUD |
| VRAM minimum | 20GB |
| VRAM optimal | 28GB |
| Best local GPU | RTX 6000 Ada 48GB |
| Cloud fallback | A100 80GB |
| Updated | 2026-02-24 |
| Data status | Verified by Real Hardware |
| Ollama source | Library reference (verified: 2026-02-24) |
| Ollama tag | gpt-oss:20b |
| Category | reasoning |
Benchmark Anchors
| Hardware | Expected tok/s |
|---|---|
| RTX 3090 24GB | 11 |
| RTX 4090 24GB | 14.9 |
| A100 80GB | 26.4 |
Quick Answers
- How much VRAM does GPT-OSS 20B CLOUD need?
- GPT-OSS 20B CLOUD needs about 20GB minimum VRAM and 28GB optimal VRAM for a safer local run target.
- Can GPT-OSS 20B CLOUD run on an RTX 3090 24GB?
- It may load on an RTX 3090 24GB because the minimum estimate is 20GB, but the safer target is 28GB. Expect tighter context or lower throughput.
- What is the Ollama command for GPT-OSS 20B CLOUD?
- Use ollama run gpt-oss:20b. Check the Ollama tag gpt-oss:20b before running.
- What is the approximate Ollama model size for GPT-OSS 20B CLOUD?
- GPT-OSS 20B CLOUD uses the gpt-oss:20b Ollama tag. Treat the CLOUD 20 profile as smaller than the runtime VRAM budget; use 20GB minimum VRAM and 28GB optimal VRAM as the safer sizing numbers.
- How much system RAM does GPT-OSS 20B CLOUD need in Ollama?
- Plan for at least 64GB system RAM alongside 20GB minimum VRAM and 28GB optimal VRAM. More RAM helps if layers spill to CPU or if you run multiple models.
- What is the RAM vs VRAM requirement for GPT-OSS 20B CLOUD?
- GPT-OSS 20B CLOUD should be sized by GPU VRAM first: 20GB minimum and 28GB optimal. System RAM should be at least 64GB so Ollama has room for CPU-side layers, context, and the surrounding app process.
- What is GPT-OSS 20B CLOUD VRAM usage on an RTX 3090?
- GPT-OSS 20B CLOUD is borderline for an RTX 3090 24GB target. The page estimates 20GB minimum and 28GB optimal VRAM, with spill risk marked as high without a larger GPU or cloud.
- Which LocalVRAM profiles share the gpt-oss:20b Ollama tag?
- LocalVRAM tracks these profiles for the same Ollama tag or model family: GPT-OSS 20B CLOUD (CLOUD, 20GB min/28GB optimal); GPT-OSS 20B Q4 (Q4, 16GB min/20GB optimal); GPT-OSS 20B Q5 (Q5, 20GB min/22GB optimal); GPT-OSS 20B Q8 (Q8, 24GB min/34GB optimal); GPT-OSS 20B FP16 (FP16, 30GB min/42GB optimal). Use the table on this page to compare quantization and VRAM targets before choosing a run command.
- Is GPT-OSS 20B CLOUD benchmark data measured or estimated?
- GPT-OSS 20B CLOUD has a verified RTX 3090 result of 156.076 tokens/s from LocalVRAM benchmark data.
Ollama Model Size and RAM Notes
| Ollama tag | gpt-oss:20b |
|---|---|
| Run command | ollama run gpt-oss:20b |
| Model size guidance | CLOUD quantization, 20 profile; size runtime by VRAM, not download size alone |
| Runtime VRAM target | 20GB minimum, 28GB optimal |
| System RAM planning | 64GB or more when running this tag locally with Ollama |
Download size alone is not a safe runtime budget. Runtime memory also includes KV cache, context length, and GPU layer placement.
Ollama RAM vs VRAM Requirements
| GPU VRAM usage estimate | 20GB minimum / 28GB optimal |
|---|---|
| System RAM requirement | 64GB or more for Ollama runtime headroom |
| RTX 3090 24GB fit | Borderline |
| CPU spill risk | High without larger GPU or cloud |
| Context warning | Longer context increases KV cache memory, so real VRAM usage can exceed short-prompt estimates. |
For Ollama, VRAM is the first fit check. System RAM matters when layers spill to CPU, when context grows, or when the local app stack runs beside the model.
Available Tag Variants and VRAM Targets
| Profile | Quantization | VRAM target | Ollama tag |
|---|---|---|---|
| GPT-OSS 20B CLOUD | CLOUD | 20GB min / 28GB optimal | gpt-oss:20b |
| GPT-OSS 20B Q4 | Q4 | 16GB min / 20GB optimal | gpt-oss:20b |
| GPT-OSS 20B Q5 | Q5 | 20GB min / 22GB optimal | gpt-oss:20b |
| GPT-OSS 20B Q8 | Q8 | 24GB min / 34GB optimal | gpt-oss:20b |
| GPT-OSS 20B FP16 | FP16 | 30GB min / 42GB optimal | gpt-oss:20b |
Ollama library tags often group multiple quantization choices under one model family. LocalVRAM separates them into VRAM profiles so you can pick the right local target.
Real Hardware Benchmark (RTX 3090)
| Tokens/s | 156.076 |
|---|---|
| Latency | 1524 ms |
| Prompt tokens | 88 |
| Eval tokens | 96 |
| Test time | 2026-04-29T05:39:58Z |
| GPU model | NVIDIA GeForce RTX 3090 |
Verified by real hardware.
Performance Curve
Reference anchors are baseline estimates. Measured RTX 3090 data is overlaid when available.
Best Hardware for GPT-OSS 20B CLOUD
- Local run: RTX 3090 (24GB) (Check latest deal) for around 156.076 tok/s on this profile.
- Cloud run: RunPod A100 80GB , about 0.2x the local 3090 speed anchor.
- Alternative cloud: Vast.ai options for flexible spot pricing.
Local vs Cloud Cost Hint
| Mode | 40h / month | 120h / month |
|---|---|---|
| Local power only (3090 baseline) | $2.24 | $6.72 |
| A100 80GB | $78 | $234 |
Related Model Profiles
- GPT-OSS 20B Q4 16GB min, 20GB optimal
- GPT-OSS 20B Q5 20GB min, 22GB optimal
- GPT-OSS 20B Q8 24GB min, 34GB optimal
- GPT-OSS 20B FP16 30GB min, 42GB optimal
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