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Contributed byMario AlkaCohere

command-a-reasoning-08-2025

Performance benchmark · measured on 27.07.2026 10:46

Benchmark-IDrun-20260727-113142-e39c6a
Timebench 3 - Kombi (Prefill + Generation)111BRuntime: llama.cppQuantisierung: Q4_K_M
Generation6,57tok/s
Prefill98,11tok/s
Time to First Token86.763,00ms
Total duration600,00s
Concurrency10parallel
Ranking in the field
This GPUAll GPUs
19of 20 systems

Performance benchmark · Primary metric: Generation-Speed (tok/s) · 10× concurrent

This run is better than 5 % of all comparable systems.
Generation 6,6 tok/s
-99 % vs Ø 464,5
Prefill 98,1 tok/s
-98 % vs Ø 5.437,0
Time to First Token 86.763 ms
+102 % vs Ø 42.890
Distribution in the field3 – 1.743 tok/s
Ø 465 Median Dieser Lauf

Wie schlägt sich dieser Benchmark mit anderen Modellen?

How does this benchmark compare on other GPUs?

Same model on different hardware · 10× concurrent · Generation (tok/s)

NVIDIA GeForce RTX 5070 Ti this runrun-20260727-113142-e39c6a
6,6 tok/s
NVIDIA GeForce RTX 3090 Tirun-20260727-151334-43b265
4,2 tok/s

Hardware

GPU: NVIDIA GeForce RTX 5070 Ti · 16 GB VRAM
CPU: AMD Ryzen Threadripper PRO 5975WX 32-Cores
RAM: 247 GB
Mainboard: ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI

Setup

Runtime: llama.cpp
Quantization: Q4_K_M
Model: command-a-reasoning-08-2025

Configuration

benchmark-konfiguration — run-20260727-113142-e39c6a
# LLM-Benchmark Konfiguration # Modell : command-a-reasoning-08-2025 # Engine : llama.cpp # Run-ID : run-20260727-113142-e39c6a # GPU : NVIDIA GeForce RTX 5070 Ti # CPU : AMD Ryzen Threadripper PRO 5975WX 32-Cores # RAM : 247 GB bench@llm-benchmark:~$ /home/godcore/llama.cpp/build/bin/llama-server \ -m /home/godcore/.cache/huggingface/hub/models--DevQuasar--CohereLabs.command-a-reasoning-08-2025-GGUF/snapshots/924408415a0e807b935d0a0c4f316a1bc40a1051/CohereLabs.command-a-reasoning-08-2025.Q4_K_M-00001-of-00005.gguf \ --alias command-a-reasoning-08-2025 \ --host 0.0.0.0 \ --port 8000 \ -ngl 12 \ -c 16384 \ -np 4 \ --jinja
Engine?Die Inferenz-Software, die das Modell ausliefert (z.B. vLLM oder llama.cpp). Sie bestimmt Geschwindigkeit, unterstuetzte Modellformate und welche Parameter ueberhaupt verfuegbar sind.llamacpp
Modellalias?Der Name, unter dem das Modell ueber die API angesprochen wird. Genau dieser Wert muss im Request-Feld 'model' stehen.command-a-reasoning-08-2025
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.16384
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird./home/godcore/.cache/huggingface/hub/models--DevQuasar--CohereLabs.command-a-reasoning-08-2025-GGUF/snapshots/924408415a0e807b935d0a0c4f316a1bc40a1051/CohereLabs.command-a-reasoning-08-2025.Q4_K_M-00001-of-00005.gguf
GPU-Layer?Anzahl der auf die GPU ausgelagerten Modell-Layer. Hoeher = mehr VRAM und schneller; der Rest laeuft auf der CPU. 999 = alles auf GPU.12
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.16384
np4

All benchmarks of this model To leaderboard

Model comparison

command-a-reasoning-08-2025 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

7,86,65,44,23,08093105118Prefill (tok/s)Generation (tok/s)NVIDIA GeForce RTX 3090 Ti - 4,2 tok/s Generation, 90 tok/s Prefill, TTFT 72.246 ms (3 Laufe)NVIDIA GeForce RTX 30...NVIDIA GeForce RTX 5070 Ti - 6,7 tok/s Generation, 108 tok/s Prefill, TTFT 64.666 ms (3 Laufe) | DIESER LAUF★ NVIDIA GeForce RTX 50...
★ NVIDIA GeForce RTX 5070 Ti 6,7 tok/s this runNVIDIA GeForce RTX 3090 Ti 4,2 tok/s

CPUby processor

7,86,65,44,23,08093105118Prefill (tok/s)Generation (tok/s)AMD Ryzen 9 8945HX with Radeon Graphics - 4,2 tok/s Generation, 90 tok/s Prefill, TTFT 72.246 ms (3 Laufe)AMD Ryzen 9 8945HX wi...AMD Ryzen Threadripper PRO 5975WX 32-Cores - 6,7 tok/s Generation, 108 tok/s Prefill, TTFT 64.666 ms (3 Laufe) | DIESER LAUF★ AMD Ryzen Threadrippe...
★ AMD Ryzen Threadripper PRO 5975WX 32-Cores 6,7 tok/s this runAMD Ryzen 9 8945HX with Radeon Graphics 4,2 tok/s

MBby mainboard

7,86,65,44,23,08093105118Prefill (tok/s)Generation (tok/s)Meigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) - 4,2 tok/s Generation, 90 tok/s Prefill, TTFT 72.246 ms (3 Laufe)Meigao Innovation Tec...ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI - 6,7 tok/s Generation, 108 tok/s Prefill, TTFT 64.666 ms (3 Laufe) | DIESER LAUF★ ASUSTeK COMPUTER INC....
★ ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 6,7 tok/s this runMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 4,2 tok/s

ENGby engine

7,37,06,76,36,09397101105Prefill (tok/s)Generation (tok/s)llama.cpp - 6,7 tok/s Generation, 99 tok/s Prefill, TTFT 68.456 ms (6 Laufe) | DIESER LAUF★ llama.cpp
★ llama.cpp 6,7 tok/s this run

DRVby driver

7,37,06,76,36,09397101105Prefill (tok/s)Generation (tok/s)unbekannt - 6,7 tok/s Generation, 99 tok/s Prefill, TTFT 68.456 ms (6 Laufe)unbekannt
unbekannt 6,7 tok/s
💰 Economics

Economics of this run

Operating cost, TCO and comparison with the next-best runs of the same model at identical concurrency (10× 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 10 W
⚡ TDP 10 W
☁️ External LLM (API)
Electricity0.30 EUR/kWh
Avg power (incl. idle)10 W missingBoard 10 W full load
Avg cost / hourEUR 0.0030
Electricity / 1M tokensEUR 0.13
Token / kWh2.37M
Acquisition (system)EUR 2,096 missingRAM EUR 1,976 · PSU EUR 120
Electricity (2 years)–
TCO (2 years)EUR 2,149
Output tokens (2 years)414.38M
☁️ External LLM (API) – comparison
External LLM cost (2 years)–
Savings vs. external (2 years)–
No power draw measured – values estimated from GPU TDP + CPU (idle + 15 %) + board.

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 (10 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

NVIDIA GeForce RTX 5070 Ti · this run 7 tok/sNVIDIA GeForce RTX 3090 Ti 4 tok/s

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.