Find which open-weight LLMs fit in your GPU or Mac and compare the speeds people report on setups like yours.
Pick your GPU or Mac, then read the speeds people reported on it, or estimate which models fit and how fast they run.
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These are individual setups, not a controlled benchmark. Compare GPU count, quantization, context and offloading before comparing speeds. How to read a report →
Exact recorded values. Context may be a configured limit; matching filters does not establish identical prompts, offloading or concurrency.
AMD RX 6600 XT · llama.cpp · 130,416 ctx
User reports prefill speed on an RX 6600 XT 8GB that does not fall steadily with context, using llama.cpp Vulkan with every layer offloaded: 935 t/s at about 1K tokens down to 318 t/s at 8K, then 472 t/s at 16K, and 89.7 t/s at 130K against 76.7 t/s at 65K. The model is not named. User says the spread across runs was under 2%.
AMD RX 6600 XT · llama.cpp · 65,536 ctx
User asks what performance P100 owners get, having ordered one for $80, and reports their current baseline on an RX 6600 XT with 32GB DDR4 3600. Current setup runs unsloth Qwen3.6 35B-A3B UD_Q4_K_XL in llama.cpp with MTP and --cpu-moe at 64k full-precision context, giving 30 t/s generation and 48-50 t/s decode, with prefill up to 800 t/s at 0 context and 700 t/s at 10k. User hopes the P100 can match the decode numbers and plans to tune -b and -ub for its higher core count; the card will go into a dedicated inference machine.
On Qwen3.8 27B at 4-bit, plain single-GPU runs. Full ranking