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All model benchmarks

Generation, 1K prompt

measured on one GPU

How fast the model writes its answer: tokens per second over 200 generated tokens, after a prompt of about 1,000 tokens. Greedy sampling, median of 10 runs.

Generation, 1K prompt

Generation, 1K prompt Generation, 1K prompt

Measured models

Model Tokens/s Interval Rank range Prompt tokens Turn time Family
SmolLM2 1.7B Instruct (Q4_K_M) 354 tok/s 352 to 361 #1 of 13 1,193 0.69 s SmolLM2
Ling 3.0 Tiny (Q4_K_M) 267 tok/s 249 to 282 #2–4 of 13 1,178 1.00 s Ling
Gemma 4 E2B Instruct QAT (UD-Q4_K_XL) 259 tok/s 259 to 260 #2–3 of 13 1,124 0.98 s Gemma
Llama 3.2 3B Instruct (Q4_K_M) 256 tok/s 256 to 257 #3–4 of 13 1,163 0.98 s Llama
Bonsai 8B (Q1_0 1-bit) 238 tok/s 237 to 238 #5 of 13 1,146 1.13 s Bonsai
Bonsai 8B (Q2_0 2-bit) 221 tok/s 220 to 222 #6 of 13 1,146 1.19 s Bonsai
Granite 4.2 3B Instruct (Q4_K_M) 200 tok/s 199 to 200 #7 of 13 1,169 1.23 s Granite
Ministral 3 3B Instruct (Q4_K_M) 189 tok/s 188 to 190 #8 of 13 1,163 1.62 s Ministral
Qwen 3.5 4B (Q4_K_M) 174 tok/s 172 to 176 #9 of 13 1,156 1.66 s Qwen
Gemma 4 E4B Instruct (Q4_K_M) 143 tok/s 143 to 145 #10 of 13 1,124 1.72 s Gemma
Llama 3.1 8B Instruct (Q4_K_M) 134 tok/s 133 to 134 #11 of 13 1,163 1.85 s Llama
Granite 4.2 8B Instruct (Q4_K_M) 112 tok/s 111 to 112 #12 of 13 1,169 2.15 s Granite
Ministral 3 8B Instruct (Q4_K_M) 105 tok/s 104 to 106 #13 of 13 1,163 2.42 s Ministral

Turn time is from sending the message to the last of 200 tokens, as the client sees it.

Model cards