Contributed byMarcel SommerZ.ai (Zhipu) glm-4.7-flash
Performance benchmark · measured on 04.08.2026 00:08
Benchmark-ID
run-20261021-093814-eee76dTimebench 3 - Kombi (Prefill + Generation)Runtime: vLLMQuantisierung: AWQ
Generation102,50tok/s
Prefill10.351,70tok/s
Time to First Token1.169,00ms
Total duration102,45s
Concurrency5parallel
Ranking in the field
932of 1469 systems
Performance benchmark · Primary metric: Generation-Speed (tok/s) · 5× concurrent
This run is better than 37 % of all comparable systems.
Generation
102,5 tok/s
-58 % vs Ø 246,5
Prefill
10.351,7 tok/s
+93 % vs Ø 5.365,8
Time to First Token
1.169 ms
-96 % vs Ø 30.155
Distribution in the field0 – 1.349 tok/s
Wie schlägt sich dieser Benchmark mit anderen Modellen?
gemma-4-E2B-itNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20260727-181606-0adf6d
1.349,3 tok/s
gemma-4-E2B-itNVIDIA GeForce RTX 5090 · run-20260728-184455-5b4937
1.301,9 tok/s
gemma-4-E2B-it3× 3x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition · run-20260728-184456-4111a9
1.195,7 tok/s
Nemotron-3-Nano-4BNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20260727-181607-30d864
1.188,7 tok/s
Nemotron-3-Nano-4BNVIDIA GeForce RTX 5090 · run-20260728-194135-5432cd
1.164,8 tok/s
gpt-oss-20bNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20260728-140954-9e81f9
1.136,3 tok/s
gpt-oss-20bNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20260730-035052-09627f
1.135,0 tok/s
Nemotron-3.5-Lightning-30B-A3BNVIDIA GeForce RTX 3090 Ti · run-20261021-093815-b22926
1.135,0 tok/s
gpt-oss-20bNVIDIA GeForce RTX 5090 · run-20260729-032121-058a31
1.113,5 tok/s
Nemotron-3-Nano-4B3× 3x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition · run-20260728-194136-836a91
1.090,6 tok/s
Nemotron-3-Nano-4B3× 3x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition · run-20260727-181606-8b381e
1.077,4 tok/s
Nemotron-3-Nano-4B3× 3x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition · run-20260728-194136-b8a818
1.071,0 tok/s
Nemotron-3-Nano-4B3× 3x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition · run-20260728-194136-038e92
1.051,1 tok/s
gpt-oss-20b3× 3x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition · run-20260729-032121-ffb18a
1.015,5 tok/s
glm-4.7-flash this runNVIDIA GeForce RTX 3090 Ti · run-20261021-093814-eee76d
102,5 tok/s
How does this benchmark compare on other GPUs?
Same model on different hardware · 5× concurrent · Generation (tok/s)
Hardware
GPU: NVIDIA GeForce RTX 3090 Ti · 24 GB VRAM
CPU: AMD Ryzen 5 5600X 6-Core Processor
RAM: 30 GB
Mainboard: ASUSTeK COMPUTER INC. PRIME A520M-K
Setup
Runtime: vLLM
Quantization: AWQ
Model: glm-4.7-flash
Configuration
# LLM-Benchmark Konfiguration
# Modell : glm-4.7-flash
# Engine : vLLM
# Run-ID : run-20261021-093814-eee76d
# GPU : NVIDIA GeForce RTX 3090 Ti
# CPU : AMD Ryzen 5 5600X 6-Core Processor
# RAM : 30 GB
bench@llm-benchmark:~$ /opt/vllm-gemma/venv/bin/python /opt/vllm-gemma/venv/bin/vllm serve cyankiwi/GLM-4.7-Flash-AWQ-4bit \
--served-model-name glm-4.7-flash-awq \
--host 192.168.41.116 \
--port 8000 \
--dtype float16 \
--max-model-len 4096 \
--gpu-memory-utilization 0.97 \
--enforce-eager
| 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. | vllm |
| Modellalias?Der Name, unter dem das Modell ueber die API angesprochen wird. Genau dieser Wert muss im Request-Feld 'model' stehen. | glm-4.7-flash-awq |
| Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann. | 4096 |
| Alias?Anzeigename des Modells nach aussen (served model name), unabhaengig vom Dateinamen. | glm-4.7-flash-awq |
| Dtype?Zahlenformat der Modellgewichte bei der Berechnung (z.B. auto, float16, bfloat16). 'auto' waehlt automatisch das vom Modell empfohlene Format. | float16 |
| Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells. | 4096 |
| GPU-Speicher?Anteil des GPU-Speichers (0 bis 1), den vLLM belegen darf. 0.92 = 92 %. Hoeher = mehr Platz fuer den KV-Cache (mehr/laengere parallele Anfragen), aber groesseres Risiko fuer 'Out of Memory'. | 0.97 |
| Enforce-Eager?Schaltet die optimierte Graph-Ausfuehrung (CUDA-/HIP-Graphs) AB und rechnet Schritt fuer Schritt. Startet schneller und spart etwas VRAM, ist im laufenden Betrieb aber meist langsamer als mit Graphs. | aktiv |
| execution_type | local |
Anzeige
Model comparison
glm-4.7-flash 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
CPUby processor
MBby mainboard
DRVby driver
unbekannt 349,3 tok/sNVIDIA 590.48.01 / CUDA 13.1 48,9 tok/s
💰 Economics
Economics of this run
Operating cost, TCO and comparison with the next-best runs of the same model at identical concurrency (5× 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
25 W
⚡ TDP
450 W
☁️ External LLM (API)
Electricity0.30 EUR/kWh
Avg power (incl. idle)450 W estimated (TDP)GPU 450 W full load
Avg cost / hourEUR 0.14
Electricity / 1M tokensEUR 0.37
Token / kWh820.00K
Acquisition (system)EUR 1,270 partial priceGPU EUR 999 · CPU EUR 121 · PSU EUR 150
Electricity (2 years)–
TCO (2 years)EUR 3,635
Output tokens (2 years)6.46B
☁️ 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 (25 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
| glm-4.7-flashNVIDIA GeForce RTX 3090 Ti | glm-4.7-flashNVIDIA GeForce RTX 3090 Ti | glm-4.7-flash2x AMD Radeon RX 7900 XTX | |
|---|---|---|---|
| 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) | – | – | – |
NVIDIA GeForce RTX 3090 Ti · this run 103 tok/sNVIDIA GeForce RTX 3090 Ti 234 tok/s2× AMD Radeon RX 7900 XTX 103 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.