Can I run gpt-oss-20b on the GeForce RTX 5060 Ti 16GB?
Runs greatMXFP4 fits in VRAM with 2.5 GB to spare: Runs great, 111.6 tok/s measured at 2k context (1 run; 8k estimate 88.1 tok/s, 70.5–105.8, calibrated estimate ±20%).
16 GB VRAM · 448.0 GB/s · FP16 23.7 TFLOPS. Assumes 32 GB of DDR5-5600 system RAM, Windows with this GPU driving the display, 8k context and an f16 KV cache.
Every quant of gpt-oss-20b on the GeForce RTX 5060 Ti 16GB
| Quant | File size | Verdict | Speed | Memory | Runs as | Notes |
|---|---|---|---|---|---|---|
| MXFP4baseline | 12.11 GB | Runs great | 111.6 tok/s8k estimate 88.1 tok/s (70.5–105.8, calibrated estimate ±20%)measured (1 run, 2k context) | 13.5 / 16.0 GB | Full GPU | — |
Reasoning models spend extra tokens thinking, so their speed thresholds are 1.5× stricter (30 / 12 / 3 tok/s).
Where the memory goes at MXFP4
- Weights
- 12.1 GB
- KV cache
- 0.2 GB
- Compute buffer
- 0.6 GB
- OS reserve
- 0.6 GB
- Free
- 2.5 GB
Context length vs. KV cache
| Context | KV f16 | KV q8_0 | KV q4_0 |
|---|---|---|---|
| 4k | 12.7 GBRuns great est. 92.2 tok/s73.8–110.6calibrated estimate ±20% | 12.7 GBRuns great est. 94.2 tok/s75.4–113.1calibrated estimate ±20% | 12.7 GBRuns great est. 95.3 tok/s76.3–114.4calibrated estimate ±20% |
| 8k | 12.9 GBRuns great 111.6 tok/s8k estimate 88.1 tok/s (70.5–105.8, calibrated estimate ±20%)measured (1 run, 2k context) | 12.8 GBRuns great est. 91.9 tok/s73.5–110.3calibrated estimate ±20% | 12.7 GBRuns great est. 94.1 tok/s75.2–112.9calibrated estimate ±20% |
| 16k | 13.2 GBRuns great est. 81.0 tok/s64.8–97.2calibrated estimate ±20% | 13.0 GBRuns great est. 87.6 tok/s70.1–105.1calibrated estimate ±20% | 12.9 GBRuns great est. 91.6 tok/s73.3–109.9calibrated estimate ±20% |
| 32k | 13.7 GBRuns great est. 69.7 tok/s55.8–83.7calibrated estimate ±20% | 13.3 GBRuns great est. 80.1 tok/s64.1–96.1calibrated estimate ±20% | 13.1 GBRuns great est. 87.1 tok/s69.7–104.5calibrated estimate ±20% |
| 64k | 14.8 GBRuns great est. 54.5 tok/s43.6–65.4calibrated estimate ±20% | 14.1 GBRuns great est. 68.4 tok/s54.7–82.1calibrated estimate ±20% | 13.7 GBRuns great est. 79.2 tok/s63.4–95.1calibrated estimate ±20% |
| 128k | 17.0 GBRuns slowly est. 18.5 tok/s12.9–24.0theoretical estimate ±30% | 15.5 GBRuns slowly est. 18.5 tok/s12.9–24.0theoretical estimate ±30% | 14.7 GBRuns great est. 67.1 tok/s53.7–80.5calibrated estimate ±20% |
S = Runs great · A = Runs well · B = Runs slowly · F = Won't run
At MXFP4 the verdict stays Runs great up to 64k context with an f16 KV cache, and up to 64k with q8_0.
Estimated speed
- Generation
- 111.6 tok/s measured at 2k context (1 run; 8k estimate 88.1 tok/s, 70.5–105.8, calibrated estimate ±20%)
- Prompt processing
- about 3,318 tok/s (1,991–4,645, rough estimate ±40%)
Measured on this exact combination
Public benchmarks we calibrate against. Their conditions (context, backend, flags) can differ from the estimates above.
| Hardware | Quant | Backend | Context | Prompt (tok/s) | Generation (tok/s) | Flags | Source | Measured |
|---|---|---|---|---|---|---|---|---|
| GeForce RTX 5060 Ti 16GB | MXFP4 | llama.cpp | 2k | 3,821.0 | 111.6 | — | github.com | 2025-08-15 |
If this is not enough
A larger model that also runs well on the GeForce RTX 5060 Ti 16GB
Runs wellQwen3.5 35B-A3B: Runs well, est. 20.4 tok/s (14.3–26.5, theoretical estimate ±30%)
How to run it
Commands for the MXFP4 file. Both tools download from Hugging Face on first run.
File: gpt-oss-20b-MXFP4.gguf (12.11 GB) from ggml-org/gpt-oss-20b-GGUF.
llama-server -hf ggml-org/gpt-oss-20b-GGUF:MXFP4 -c 8192 -ngl all -fa on- -ngl all loads every layer on the GPU.
- -fa on enables flash attention (needed for KV cache quantization).
- --cache-type-k q8_0 --cache-type-v q8_0 shrinks the KV cache to 0.11 GB at 8k context.
PowerShell (quit the Ollama tray app first):
$env:OLLAMA_CONTEXT_LENGTH="8192"; ollama serve
ollama run hf.co/ggml-org/gpt-oss-20b-GGUF:MXFP4- Ollama defaults to 4k context on GPUs under 24 GB. Set OLLAMA_CONTEXT_LENGTH when starting the server, or /set parameter num_ctx inside the chat.
/set parameter num_ctx 8192
Related pages
Guide: How much VRAM do local LLMs need? 8–32 GB tiers (2026)
Other models on the GeForce RTX 5060 Ti 16GB
gpt-oss-20b on other hardware
Frequently asked questions
Can I run gpt-oss-20b on the GeForce RTX 5060 Ti 16GB?
MXFP4 fits in VRAM with 2.5 GB to spare: Runs great, 111.6 tok/s measured at 2k context (1 run; 8k estimate 88.1 tok/s, 70.5–105.8, calibrated estimate ±20%). MXFP4 needs 12.9 GB at 8k context; this setup has 15.4 GB of usable memory and 28.0 GB of free system RAM.
How much context can gpt-oss-20b use on the GeForce RTX 5060 Ti 16GB?
At MXFP4 the verdict stays Runs great up to 64k context with an f16 KV cache (1.6 GB of KV) and up to 64k with a q8_0 KV cache (0.9 GB). The model supports up to 128k.
Which quant should I use, and how fast is it?
MXFP4 (12.11 GB) is the only tracked file: Runs great, 111.6 tok/s measured at 2k context (1 run; 8k estimate 88.1 tok/s, 70.5–105.8, calibrated estimate ±20%).
Speeds are estimates from memory bandwidth, calibrated against public benchmarks, and each one comes with an error band and a confidence label. Real results vary with drivers, backend, context length and thermals.