Local model performance on your hardware

generation:

165.6 tokens/s

prompt processing (prefill):

9555.9 tokens/s

quant:

Q4_K_M (GGUF)

User reports 165.64 t/s generation and 9555.89 t/s prompt processing. Source evidence (verbatim): Source: https://blog.majid.info/llama-bench/golgotha.txt # NVIDIA GeForce RTX 5090 Laptop GPU 24GB: original Gemma4 E4B Q4_K_M llama-bench log First-hand author log linked from https://blog.majid.info/llama-bench/ (April 13, 2026 article). Measured hardware: NVIDIA GeForce RTX 5090 Laptop GPU 24GB. Gemma4 E4B Q4_K_M GGUF, llama.cpp e3f542d8 (4236), backend CUDA. pp512 9555.89 ±101.42 tokens/s; tg128 165.64 ±0.19 tokens/s. One device, ngl -1; 23983 MiB visible VRAM, not resident use. Laptop GPU, not desktop RTX5090. CPU and system RAM unknown. Command names --hf-repo unsloth/gemma-4-E4B-it-GGUF; exact tested quantization repository https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF ; tested revision unknown. Log reports 4.62 GiB model size and 7.52B counted parameters; size is not resident memory. Context depth, KV quantization, measured power, driver version and explicit speculation/concurrency declarations unreported. Raw source: https://blog.majid.info/llama-bench/golgotha.txt .

generation:

30.0 tokens/s

quant:

Q4_K_M (GGUF)

kv:

8bit

User reports Qwen3.8 27B at a steady 30 t/s on an RTX 5090 Laptop 24GB. Setup is llama.cpp with Q4_K_M and 8-bit KV cache at 65k context, using 21 GB of VRAM. The user also tested Unsloth UD_Q5_K_XL at 27 t/s, and NInfer models reaching 82.54 t/s (8-bit, 132k context), 92.4 t/s (NVFP4, 32k context), and 86.63 t/s (NVFP4, 4-bit, 64k context). The user says the laptop is too slow for coding and recommends a DGX Spark instead.