ByteDance Seed
Seed-OSS-36B-Instruct
Performancebenchmark · gemessen am 23.07.2026 02:20
Benchmark-ID
run-20260723-023424-47e9f4Timebench 3 - Kombi (Prefill + Generation)Runtime: llama.cppQuantisierung: Q4_K_M
Generation0,97tok/s
Prefill7,84tok/s
Time to First Token240.790,00ms
Gesamtdauer600,00s
Concurrency1parallel
Einordnung im Feld
235von 239 Systemen
Performancebenchmark · Leitmetrik: Generation-Speed (tok/s)
Dieser Lauf ist besser als 2 % aller vergleichbaren Systeme.
Generation
1,0 tok/s
-99 % vs Ø 96,3
Prefill
7,8 tok/s
-100 % vs Ø 3.518,1
Time to First Token
240.790 ms
+1.400 % vs Ø 16.050
Verteilung im Feld0 – 1.295 tok/s
Wie schlägt sich dieser Benchmark?
gpt-oss-20bNVIDIA GeForce RTX 5090 · run-20260723-045447-edeed3
1.295,3 tok/s
Qwen3-Coder-30B-A3B-InstructNVIDIA GeForce RTX 5090 · run-20260723-045450-366dae
966,2 tok/s
Qwen3-30B-A3B-Instruct-2507NVIDIA GeForce RTX 5090 · run-20260723-045450-e8df4d
909,3 tok/s
Qwen3-30B-A3B-Thinking-2507NVIDIA GeForce RTX 5090 · run-20260723-045450-14f178
873,7 tok/s
gpt-oss-20bNVIDIA GeForce RTX 5090 · run-20260723-045447-5656a1
784,5 tok/s
Qwen3-Coder-30B-A3B-InstructNVIDIA GeForce RTX 5090 · run-20260723-045450-4162ec
688,6 tok/s
Qwen3-30B-A3B-Instruct-2507NVIDIA GeForce RTX 5090 · run-20260723-045448-81246f
655,8 tok/s
Qwen3-30B-A3B-Thinking-2507NVIDIA GeForce RTX 5090 · run-20260723-045450-1d48c8
633,4 tok/s
gpt-oss-20b3× AMD Radeon AI PRO R9700 · run-20260723-023425-4d6891
340,5 tok/s
gpt-oss-20b3× AMD Radeon AI PRO R9700 · run-20260723-023425-189e09
331,7 tok/s
North-Mini-Code-1.03× AMD Radeon AI PRO R9700 · run-20260723-023427-9af096
279,0 tok/s
NVIDIA-Nemotron-3-Nano-30B-A3B3× AMD Radeon AI PRO R9700 · run-20260723-031252-92db54
274,0 tok/s
Nemotron-Cascade-2-30B-A3B3× AMD Radeon AI PRO R9700 · run-20260723-031252-286996
270,4 tok/s
Nemotron-3-Nano-Omni-30B-A3B-Reasoning3× AMD Radeon AI PRO R9700 · run-20260723-031253-e2aa7d
268,7 tok/s
Seed-OSS-36B-Instruct dieser LaufKeine GPU (CPU-only) · run-20260723-023424-47e9f4
1,0 tok/s
Hardware
GPU: Keine GPU (CPU-only)
CPU: 51x Intel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz
RAM: 504 GB
Mainboard: Dell Inc. PowerEdge R820
Setup
Runtime: llama.cpp
Quantisierung: Q4_K_M
Betriebssystem: Ubuntu 24.04.3 LTS (Kernel 7.0.0-28-generic)
Modell: Seed-OSS-36B-Instruct
Konfiguration
# LLM-Benchmark Konfiguration
# Modell : Seed-OSS-36B-Instruct
# Engine : llama.cpp
# Run-ID : run-20260723-023424-47e9f4
# GPU : Keine GPU (CPU-only)
# CPU : 51x Intel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz
# RAM : 504 GB
bench@llm-benchmark:~$ /opt/llama.cpp/build/bin/llama-server \
-m /opt/models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf \
--host 10.220.231.216 \
--port 8081 \
--numa distribute \
-t 64 \
-tb 64 \
-c 8192
| Engine | llamacpp |
| Kontextlaenge | 8192 |
| Modellpfad | /opt/models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf |
| NUMA | distribute |
| Threads | 64 |
| Batch-Threads | 64 |
| Kontext | 8192 |