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Leaderboard/models/gemma-2-9b-it

gemma-2-9b-it-4bit

7 workload results across 3 hardware configurations.

Fastest local config

152.7 decode tok/s

on RTX 5090 (32GB) + AMD Ryzen 7 9850X3D 8-Core Processor (8c) + 30GB via llama.cpp. see full run

For a standard 4-bit quantization using MLX, here are measured results for short-chat generation.

On MLX 4-bit, gemma-2-9b-it is fastest on the M3 Ultra (60-core GPU) at 89.45 tok/s, 307% faster than the M3 Pro (18-core GPU) (21.97 tok/s).

CardDecode tok/sTTFT (118-token prompt)Run
M3 Ultra (60-core GPU)89.45 (single run)691 msr_iz137eqvuzy
M3 Pro (18-core GPU)21.97 (single run)1151 msr_q7t7b7dcuz5

gemma-2-9b-it, 4-bit, MLX, chat-short, batch 1. Only cards with their standard memory are listed; CPU-only hosts are excluded.

Local runs (7 workload results)

Runs from contributors' own machines via MLX, llama.cpp, vLLM, exllamav2, or ollama. Signed on the submitter's hardware.

RTX 5090 (32GB) + AMD Ryzen 7 9850X3D 8-Core Processor (8c) + 30GBRTX 5090 (32GB) + AMD Ryzen 7 9850X3D 8-Core Processor (8c) + 30GB

WorkloadBackendQuantdecode tok/sprefill tok/sTTFTRun
chat-shortllama.cpp-69.45tok/sno data325msr_1_xl4zb5-xj
chat-longllama.cpp-69.42tok/sno data1,044msr_1_xl4zb5-xj
concurrent-decodellama.cpp-68.57tok/sno datano datar_1_xl4zb5-xj
agent-tracellama.cpp-68.46tok/s4,816.8tok/s420msr_1_xl4zb5-xj
chat-shortllama.cpp-152.7tok/sno data58.9msr__b_bzmmab_8

M3 Pro (18-core GPU) + 36GB unifiedM3 Pro (18-core GPU) + 36GB unified

WorkloadBackendQuantdecode tok/sprefill tok/sTTFTRun
chat-shortmlx@0.31.3-21.97tok/s102.5tok/s1,151msr_q7t7b7dcuz5

M3 Ultra (60-core GPU) + 96GB unifiedM3 Ultra (60-core GPU) + 96GB unified

WorkloadBackendQuantdecode tok/sprefill tok/sTTFTRun
chat-shortmlx@0.31.3-89.45tok/s170.7tok/s691msr_iz137eqvuzy

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