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Contributed byMario AlkaAleph Alpha

Kolibri-1

Performance benchmark · measured on 08.10.2026 17:52

Benchmark-IDrun-20261008-165122-8608d9
Kolibri Prefill-Decode 1024-256 - Summenraten C1 C5 C10 V1MoE78.1BRuntime: serverphasetimingQuantisierung: NVFP4
ℹ
Public note: Summenrate fuer C1 = C mal gewichtete serverseitige Phasenrate je Anfrage. Kein End-to-End-Batchdurchsatz. Individuelle vLLM-Phasenzeiten sind nicht vorhanden; dies ist eine normierte Summenrate, nicht die nachtraeglich rekonstruierte Summe einzelner Threadraten. Originale Anfrageraten und Batches bleiben im Messprotokoll. Modell einheitlich Kolibri-1; Quantisierung im separaten Feld.
Generation133,13tok/s
Prefill20.589,83tok/s
Time to First Token106,00ms
Total duration6,06s
Concurrency1parallel
Ranking in the field
343of 1808 systems

Performance benchmark · Primary metric: Generation-Speed (tok/s) · 1× concurrent

This run is better than 81 % of all comparable systems.
Generation 133,1 tok/s
+73 % vs Ø 77,1
Prefill 20.589,8 tok/s
+626 % vs Ø 2.836,4
Time to First Token 106 ms
-100 % vs Ø 25.162
Distribution in the field0 – 405 tok/s
Ø 77 Median Dieser Lauf

Wie schlägt sich dieser Benchmark mit anderen Modellen?

How does this benchmark compare on other GPUs?

Same model on different hardware · 1× concurrent · Generation (tok/s)

Configuration

benchmark-konfiguration — run-20261008-165122-8608d9
# LLM-Benchmark Konfiguration # Modell : Kolibri-1 # Run-ID : run-20261008-165122-8608d9 # GPU : NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition # CPU : AMD Ryzen 9 7945HX with Radeon Graphics # RAM : 60 GB bench@llm-benchmark:~$ cat benchmark.conf execution_type local
execution_typelocal

All benchmarks of this model To leaderboard

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Model comparison

Kolibri-1 on various hardware

All published performance runs of this model – each bubble a variant: position = prefill (X) × generation (Y), bubble size = number of runs. Closer to the top right = faster. ★ Marked gold = this benchmark.

GPUby graphics card

1.3269956633320,0014.11528.23142.346Prefill (tok/s)Generation (tok/s)AMD Radeon 8060S Graphics - 158,2 tok/s Generation, 1.648 tok/s Prefill, TTFT 3.695 ms (3 Laufe)AMD Radeon 8060S Grap...NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition - 1.044,4 tok/s Generation, 34.389 tok/s Prefill, TTFT 192 ms (12 Laufe) | DIESER LAUF★ NVIDIA RTX PRO 6000 B...
★ NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 1.044,4 tok/s this runAMD Radeon 8060S Graphics 158,2 tok/s

CPUby processor

1.3269956633320,0014.11528.23142.346Prefill (tok/s)Generation (tok/s)AMD RYZEN AI MAX+ 395 w/ Radeon 8060S - 158,2 tok/s Generation, 1.648 tok/s Prefill, TTFT 3.695 ms (3 Laufe)AMD RYZEN AI MAX+ 395...AMD Ryzen 9 7945HX with Radeon Graphics - 1.044,4 tok/s Generation, 34.389 tok/s Prefill, TTFT 192 ms (12 Laufe) | DIESER LAUF★ AMD Ryzen 9 7945HX wi...
★ AMD Ryzen 9 7945HX with Radeon Graphics 1.044,4 tok/s this runAMD RYZEN AI MAX+ 395 w/ Radeon 8060S 158,2 tok/s

MBby mainboard

1.3269956633320,0014.11528.23142.346Prefill (tok/s)Generation (tok/s)Meigao Innovation Technology (Shen Zhen) Co., Ltd SHWSA (MS-S1 MAX) - 158,2 tok/s Generation, 1.648 tok/s Prefill, TTFT 3.695 ms (3 Laufe)Meigao Innovation Tec...Shenzhen Meigao Electronic Equipment Co.,Ltd DRFXI (MotherBoard Series) - 1.044,4 tok/s Generation, 34.389 tok/s Prefill, TTFT 192 ms (12 Laufe) | DIESER LAUF★ Shenzhen Meigao Elect...
★ Shenzhen Meigao Electronic Equipment Co.,Ltd DRFXI (MotherBoard Series) 1.044,4 tok/s this runMeigao Innovation Technology (Shen Zhen) Co., Ltd SHWSA (MS-S1 MAX) 158,2 tok/s

ENGby engine

1.1771.07597487277007.44814.89522.343Prefill (tok/s)Generation (tok/s)unbekannt - 1.044,4 tok/s Generation, 18.018 tok/s Prefill, TTFT 1.967 ms (6 Laufe)unbekanntvLLM - 902,9 tok/s Generation, 0 tok/s Prefill, TTFT 177 ms (9 Laufe)vLLM
unbekannt 1.044,4 tok/svLLM 902,9 tok/s

DRVby driver

1.1491.0971.04499294016.93717.65818.37919.099Prefill (tok/s)Generation (tok/s)unbekannt - 1.044,4 tok/s Generation, 18.018 tok/s Prefill, TTFT 893 ms (15 Laufe)unbekannt
unbekannt 1.044,4 tok/s
💰 Economics

Economics of this run

Operating cost, TCO and comparison with the next-best runs of the same model at identical concurrency (1× concurrent). Methodology →

⚙️ ConfigurationAll metrics and charts below follow these settings – based on a 24-month runtime.Save to URLReset
⚡ Electricity price EUR/kWh
⚙️ System utilization 100 %
🖥️ Acquisition EUR
🔌 Idle 30 W
⚡ TDP 300 W
☁️ External LLM (API)
Electricity0.30 EUR/kWh
Avg power (incl. idle)300 W estimated (TDP)GPU 300 W full load
Avg cost / hourEUR 0.090
Electricity / 1M tokensEUR 0.19
Token / kWh1.60M
Acquisition (system)EUR 13,120 partial priceGPU EUR 13,000 · PSU EUR 120
Electricity (2 years)–
TCO (2 years)EUR 14,697
Output tokens (2 years)8.40B
☁️ External LLM (API) – comparison
External LLM cost (2 years)–
Savings vs. external (2 years)–

All values above and the charts below take the configured system utilization into account: at X% the system generates only X% of the time, the rest it idles (30 W). Cost per hour drops (more idle), cost per token rises.

Cost over 2 years – electricity only

Cost over 2 years – incl. acquisition (TCO)

Speed vs. tokens per euro

Euro per 1M tokens

Comparison vs. API – economics per benchmark

Kolibri-1NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation EditionKolibri-1NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation EditionKolibri-1NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation EditionKolibri-1AMD Radeon 8060S Graphics
Electricity cost (24 mo.)––––
Acquisition cost––––
Total cost (TCO)––––
Generated tokens (24 mo.)––––
Token price via API––––
Break-even point (days)––––
Result (savings / extra cost)––––

Comparison with up to 3 next-best runs of this model at the same concurrency (at least one on different hardware). Power = GPU TDP + CPU (idle + 15 %) + board (estimated), acquisition = full system (GPU + CPU + board + RAM + PSU), prices = stored market prices.

Contributed by

Mario Alka Administrator

@marioalka

Ich bin Unternehmer, Softwareentwickler und KI-Enthusiast. Seit vielen Jahren entwickle ich Unternehmenssoftware und beschäftige mich inzwischen fast täglich mit lokalen LLMs, KI-Agenten und leistungsfähiger KI-Hardware.

Mit LLM-Benchmark.de möchte ich eine Plattform schaffen, auf der Modelle, GPUs und Agenten objektiv und reproduzierbar miteinander verglichen werden.