Gemma 2 27B IT vs GPT OSS 20B
GPT OSS 20B leads llmrun Score 96.4 to 72.7; both fit 24 GB at Q4_K_M; GPT OSS 20B is 5.1× faster on RTX 4090 (~190 vs ~37 tok/s).
Model B · OpenAI
GPT OSS 20B20.9B · 4.2B active · 128K context · Apache 2.0 · Aug 2025
Choose GPT OSS 20B if you want…
Higher llmrun Score
96.4 vs 72.7
Ahead on the benchmarks
3 of 3 shared boards
Fits a 12 GB GPU
IQ4_XS · 11.8 GB; Gemma 2 27B IT needs 9.8 GB at its smallest quant
5.1× faster on RTX 4090
~190 vs ~37 tok/s
Lighter on memory
12.9 vs 18.0 GB at Q4_K_M
Longer context window
128K vs 8K tokens
Gemma 2 27B IT: no clear edge over GPT OSS 20B in the data we have.
On your hardware
Each model at its default quant (Q4_K_M), a smaller one when it must. Estimates, ±20%.
8 GB
RTX 4060 · 8 GB tier
Gemma 2 27B IT
Doesn't fit
smallest quant needs 9.8 GB
GPT OSS 20B
IQ2_M · ~103 tok/s
7.6 GB · up to 32Kfits
12 GB
RTX 3060 12GB · 12 GB tier
Gemma 2 27B IT
IQ2_M · ~23 tok/s
11.7 GB · up to 8K
GPT OSS 20B
IQ4_XS · ~89 tok/s
11.8 GB · up to 16Kbetter quant
16 GB
RTX 5060 Ti 16GB · 16 GB tier
Gemma 2 27B IT
IQ3_M · ~22 tok/s
15 GB · up to 8K
GPT OSS 20B
Q4_K_M · ~98 tok/s
13.1 GB · up to 128Kmore headroom
24 GBreference
RTX 4090 · 24 GB tier
Gemma 2 27B IT
Q4_K_M · ~37 tok/s
19.5 GB · up to 8K
GPT OSS 20B
Q4_K_M · ~190 tok/s
13.1 GB · up to 128Kfaster
32 GB
RTX 5090 · 32 GB tier
Gemma 2 27B IT
Q4_K_M · ~65 tok/s
19.5 GB · up to 8K
GPT OSS 20B
Q4_K_M · ~282 tok/s
13.1 GB · up to 128Kfaster
48 GB
RTX A6000 · 48 GB tier
Gemma 2 27B IT
Q4_K_M · ~28 tok/s
19.5 GB · up to 8K
GPT OSS 20B
Q4_K_M · ~154 tok/s
13.1 GB · up to 128Kfaster
Mac 32 GB
Mac Mini M4 · Macs
Gemma 2 27B IT
Q4_K_M · ~5 tok/s
19.5 GB · up to 8K
GPT OSS 20B
Q4_K_M · ~28 tok/s
13.1 GB · up to 128Kfaster
Mac 64 GB
Mac Studio M4 Max · Macs
Gemma 2 27B IT
Q4_K_M · ~21 tok/s
19.5 GB · up to 8K
GPT OSS 20B
Q4_K_M · ~86 tok/s
13.1 GB · up to 128Kfaster
Mac 128 GB
Mac Studio M4 Max · Macs
Gemma 2 27B IT
Q4_K_M · ~21 tok/s
19.5 GB · up to 8K
GPT OSS 20B
Q4_K_M · ~86 tok/s
13.1 GB · up to 128Kfaster
Reference GPU: RTX 4090. Tinted cell = the better pick on that machine (fits, more headroom, or at least 10% faster).
Overall
Gemma 2 27B IT
GPT OSS 20B
Coding
Gemma 2 27B IT
no data
GPT OSS 20B
Science
Gemma 2 27B IT
no data
GPT OSS 20B
Reasoning
Gemma 2 27B IT
no data
GPT OSS 20B
Gemma 2 27B IT · #97 of 106 open models · #29 of 36 that fit 24 GB
GPT OSS 20B · #69 of 106 open models · #16 of 36 that fit 24 GB
Benchmarks
3 shared boards: Gemma 2 27B IT leads 0, GPT OSS 20B leads 3 · Benchmarks imported Nov 2026
Gemma 2 27B IT
36.5%#76/100 open
IndependentEpoch AI · Jun 2024
GPT OSS 20B
60.8%#44/100 open
IndependentEpoch AI · Aug 2025
Gemma 2 27B IT
1.4%#80/88 open
IndependentEpoch AI · Jun 2024
GPT OSS 20B
65.3%#36/88 open
IndependentEpoch AI · Aug 2025
Gemma 2 27B IT
56.5%#62/146 open
IndependentTIGER-Lab
GPT OSS 20B
73.6%#33/146 open
IndependentTIGER-Lab
10 more boards (other shared and single-model boards)
Gemma 2 27B IT
1289.5#113/188 open
ArenaLMArena · Oct 2026
GPT OSS 20B
1317.3#98/188 open
ArenaLMArena · Oct 2026
Only Gemma 2 27B IT (2)
- MATH Level 527.9%
- MMLU75.7%
VRAM by quantization
Memory to load the model and 2K context; below it, the total at 32K and 128K context. Lighter side in bold.
Q2_K
Gemma 2 27B IT
12.7 GB10.4 GB file
32K — · 128K —
GPT OSS 20B
9.3 GB11.5 GB file
32K 9.8 GB · 128K 11.5 GB
Q3_K_S
Gemma 2 27B IT
13.1 GB12.2 GB file
32K — · 128K —
GPT OSS 20B
9.5 GB11.5 GB file
32K 10.0 GB · 128K 11.7 GB
Q3_K_M
Gemma 2 27B IT
14.6 GB13.4 GB file
32K — · 128K —
GPT OSS 20B
10.6 GB11.5 GB file
32K 11.1 GB · 128K 12.8 GB
Q4_0
Gemma 2 27B IT
15.0 GB15.7 GB file
32K — · 128K —
GPT OSS 20B
10.8 GB11.5 GB file
32K 11.3 GB · 128K 13.0 GB
Q4_K_M
Gemma 2 27B IT
18.0 GB16.6 GB file
32K — · 128K —
GPT OSS 20B
12.9 GB11.6 GB file
32K 13.4 GB · 128K 15.1 GB
Q5_K_M
Gemma 2 27B IT
21.3 GB19.4 GB file
32K — · 128K —
GPT OSS 20B
15.3 GB11.7 GB file
32K 15.8 GB · 128K 17.5 GB
Q6_K
Gemma 2 27B IT
24.7 GB22.3 GB file
32K — · 128K —
GPT OSS 20B
17.6 GB12.0 GB file
32K 18.1 GB · 128K 19.8 GB
Q8_0
Gemma 2 27B IT
30.0 GB28.9 GB file
32K — · 128K —
GPT OSS 20B
21.3 GB~20.9 GB file
32K 21.8 GB · 128K 23.5 GB
19 more quants
IQ2_XXS
Gemma 2 27B IT
8.2 GB7.6 GB file
32K — · 128K —
GPT OSS 20B
6.1 GB~5.8 GB file
32K 6.6 GB · 128K 8.3 GB
IQ2_XS
Gemma 2 27B IT
9.0 GB8.4 GB file
32K — · 128K —
GPT OSS 20B
6.7 GB~6.3 GB file
32K 7.2 GB · 128K 8.9 GB
IQ2_S
Gemma 2 27B IT
9.4 GB8.7 GB file
32K — · 128K —
GPT OSS 20B
6.9 GB~6.5 GB file
32K 7.4 GB · 128K 9.1 GB
IQ2_M
Gemma 2 27B IT
10.1 GB9.4 GB file
32K — · 128K —
GPT OSS 20B
7.4 GB~7.1 GB file
32K 7.9 GB · 128K 9.6 GB
IQ3_XXS
Gemma 2 27B IT
11.6 GB10.8 GB file
32K — · 128K —
GPT OSS 20B
8.5 GB11.6 GB file
32K 9.0 GB · 128K 10.7 GB
Q2_K_S
Gemma 2 27B IT
not listed
GPT OSS 20B
8.7 GB12.1 GB file
32K 9.2 GB · 128K 10.9 GB
IQ3_XS
Gemma 2 27B IT
12.4 GB11.6 GB file
32K — · 128K —
GPT OSS 20B
9.0 GB11.6 GB file
32K 9.5 GB · 128K 11.2 GB
IQ3_S
Gemma 2 27B IT
12.7 GB12.2 GB file
32K — · 128K —
GPT OSS 20B
9.3 GB12.1 GB file
32K 9.8 GB · 128K 11.5 GB
IQ3_M
Gemma 2 27B IT
13.5 GB12.5 GB file
32K — · 128K —
GPT OSS 20B
9.8 GB11.6 GB file
32K 10.3 GB · 128K 12.0 GB
Q3_K_L
Gemma 2 27B IT
15.4 GB14.5 GB file
32K — · 128K —
GPT OSS 20B
11.1 GB11.5 GB file
32K 11.6 GB · 128K 13.3 GB
IQ4_XS
Gemma 2 27B IT
16.1 GB14.8 GB file
32K — · 128K —
GPT OSS 20B
11.6 GB11.6 GB file
32K 12.1 GB · 128K 13.8 GB
IQ4_NL
Gemma 2 27B IT
not listed
GPT OSS 20B
12.1 GB11.6 GB file
32K 12.6 GB · 128K 14.3 GB
Q4_1
Gemma 2 27B IT
not listed
GPT OSS 20B
12.1 GB11.6 GB file
32K 12.6 GB · 128K 14.3 GB
Q4_K_S
Gemma 2 27B IT
16.9 GB15.7 GB file
32K — · 128K —
GPT OSS 20B
12.1 GB11.6 GB file
32K 12.6 GB · 128K 14.3 GB
Q4_K_L
Gemma 2 27B IT
18.3 GB16.9 GB file
32K — · 128K —
GPT OSS 20B
13.2 GB11.9 GB file
32K 13.7 GB · 128K 15.4 GB
Q5_0
Gemma 2 27B IT
18.7 GB18.9 GB file
32K — · 128K —
GPT OSS 20B
not listed
Q5_K_S
Gemma 2 27B IT
20.6 GB18.9 GB file
32K — · 128K —
GPT OSS 20B
14.8 GB11.7 GB file
32K 15.3 GB · 128K 17.0 GB
Q5_K_L
Gemma 2 27B IT
21.7 GB19.7 GB file
32K — · 128K —
GPT OSS 20B
15.5 GB11.9 GB file
32K 16.0 GB · 128K 17.7 GB
BF16
Gemma 2 27B IT
59.9 GB~54.5 GB file
32K — · 128K —
GPT OSS 20B
42.2 GB~41.8 GB file
32K 42.7 GB · 128K 44.4 GB
Gemma 2 27B IT
Ollama · Q4_K_M
ollama run hf.co/bartowski/gemma-2-27b-it-GGUF:Q4_K_MGPT OSS 20B
Ollama · Q4_K_M
ollama run hf.co/unsloth/gpt-oss-20b-GGUF:Q4_K_M| Spec | Gemma 2 27B IT | GPT OSS 20B |
|---|---|---|
| Publisher | OpenAI | |
| Parameters | 27.2B | 20.9B |
| Active parameters | — | 4.2B per token |
| Architecture | — | GptOssForCausalLM |
| Context | 8K tokens | 128K tokens |
| Modalities | Chat | Chat, Function-calling |
| License | Gemma Terms | Apache 2.0 |
| Released | Jun 2024 | Aug 2025 |
Frequently Asked Questions
Which is better, Gemma 2 27B IT or GPT OSS 20B?
GPT OSS 20B leads llmrun Score 96.4 to 72.7; both fit 24 GB at Q4_K_M; GPT OSS 20B is 5.1× faster on RTX 4090 (~190 vs ~37 tok/s). Choose GPT OSS 20B for higher llmrun Score and ahead on the benchmarks.
Which needs less VRAM, Gemma 2 27B IT or GPT OSS 20B?
At Q4_K_M, Gemma 2 27B IT requires 18.0 GB while GPT OSS 20B needs 12.9 GB, making GPT OSS 20B the easier model to run on your local setup.
Which is faster on RTX 4090?
Estimated decode speed on RTX 4090 is about 37 tok/s for Gemma 2 27B IT (Q4_K_M) and 190 tok/s for GPT OSS 20B (Q4_K_M), so GPT OSS 20B is faster. Estimates, ±20%.
Can I run Gemma 2 27B IT or GPT OSS 20B on a 16 GB GPU?
Gemma 2 27B IT fits at IQ3_M; GPT OSS 20B fits at Q4_K_M.
Which has a longer context window, Gemma 2 27B IT or GPT OSS 20B?
Gemma 2 27B IT supports 8,192 tokens and GPT OSS 20B supports 131,072 tokens.