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llm-speed
Leaderboard/models/stable-code-instruct-3b

stable-code-instruct-3b-4bit

7 workload results across 3 hardware configurations.

Fastest local config

356.1 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, stable-code-instruct-3b is fastest on the M3 Ultra (60-core GPU) at 192.5 tok/s, 894% faster than the M3 Pro (18-core GPU) (19.37 tok/s).

CardDecode tok/sTTFT (127-token prompt)Run
M3 Ultra (60-core GPU)192.5 (single run)226 msr_y2_5y8oo97d
M3 Pro (18-core GPU)19.37 (single run)967 msr_pqjsvd-cub4

stable-code-instruct-3b, 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-341.9tok/sno data175msr_q9f15lz6831
chat-longllama.cpp-289.1tok/sno data232msr_q9f15lz6831
concurrent-decodellama.cpp-356.1tok/sno datano datar_q9f15lz6831
agent-tracellama.cpp-292.8tok/s44,387.5tok/s42.7msr_q9f15lz6831
chat-shortllama.cpp-331.0tok/sno data36.7msr_8l57cim1i10

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

WorkloadBackendQuantdecode tok/sprefill tok/sTTFTRun
chat-shortmlx@0.31.3-192.5tok/s560.7tok/s226msr_y2_5y8oo97d

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

WorkloadBackendQuantdecode tok/sprefill tok/sTTFTRun
chat-shortmlx@0.31.3-19.37tok/s131.3tok/s967msr_pqjsvd-cub4

stable-code-instruct-3b-4bit on hardware