considering buying hardware to run everything locally. Would should I buy? #asknostr
r/LocalLlama is the best resource for this question
tldr: For agentic-type tasks in the background, probably an apple M series with lots of VRAM. And Qwen 3.5 27b has reached a level of agentic effectiveness that can run on a single 3090 that is kinda staggering (something like opus 4.1)
rough breakdown:
For most cost effective+fastest, stack 3090s until you have enough VRAM to run the model size class you want.
For most cost effective/easiest/most power efficient, buy an M1 Max with as much VRAM as you need to run the models you want (I recently got a 64gb M1 Max for $1200 that runs Qwen 3.5 122b at about 200 t/s prompt processing, 20 t/s generation. Running continuous openclaw cron jobs in the background, sipping power, not heating up the room or making any noise, love it)
For a bigger budget+fastest, stack 5090s (32gb), or if you don't want so many gpus to physically manage, NVIDIA RTX Pro 6000 Blackwell (96gb).
For bigger budget+easiest, M3 Ultra or M4 Max with lots of VRAM, or wait for M5 Ultra/max. Performance comparison: https://github.com/ggml-org/llama.cpp/discussions/4167
AMD is a place to go to increase cost effectiveness in exchange for more software management headache. Ryzen AI Max+ 395 128gb is an interesting alternative to large-vram mac setups for running big models with minimal hardware and max power efficiency.
comparison of relevant models: https://artificialanalysis.ai/models?models=gpt-5-4-mini%2Cgpt-oss-120b%2Cgpt-oss-20b%2Cgpt-5-4-pro%2Cgpt-5-4%2Cgemini-3-flash-reasoning%2Cgemma-4-31b%2Cgemini-3-1-pro-preview%2Cgemini-3-1-flash-lite-preview%2Cclaude-4-5-haiku-reasoning%2Cclaude-opus-4-6-adaptive%2Cclaude-sonnet-4-6-adaptive%2Cdeepseek-v3-2-reasoning%2Cgrok-4-20%2Cminimax-m2-7%2Cglm-5%2Cqwen3-5-397b-a17b%2Cqwen3-5-122b-a10b%2Cqwen3-5-35b-a3b%2Cqwen3-5-27b%2Cclaude-4-opus-thinking%2Cclaude-4-1-opus-thinking
tldr: For agentic-type tasks in the background, probably an apple M series with lots of VRAM. And Qwen 3.5 27b has reached a level of agentic effectiveness that can run on a single 3090 that is kinda staggering (something like opus 4.1)
rough breakdown:
For most cost effective+fastest, stack 3090s until you have enough VRAM to run the model size class you want.
For most cost effective/easiest/most power efficient, buy an M1 Max with as much VRAM as you need to run the models you want (I recently got a 64gb M1 Max for $1200 that runs Qwen 3.5 122b at about 200 t/s prompt processing, 20 t/s generation. Running continuous openclaw cron jobs in the background, sipping power, not heating up the room or making any noise, love it)
For a bigger budget+fastest, stack 5090s (32gb), or if you don't want so many gpus to physically manage, NVIDIA RTX Pro 6000 Blackwell (96gb).
For bigger budget+easiest, M3 Ultra or M4 Max with lots of VRAM, or wait for M5 Ultra/max. Performance comparison: https://github.com/ggml-org/llama.cpp/discussions/4167
AMD is a place to go to increase cost effectiveness in exchange for more software management headache. Ryzen AI Max+ 395 128gb is an interesting alternative to large-vram mac setups for running big models with minimal hardware and max power efficiency.
comparison of relevant models: https://artificialanalysis.ai/models?models=gpt-5-4-mini%2Cgpt-oss-120b%2Cgpt-oss-20b%2Cgpt-5-4-pro%2Cgpt-5-4%2Cgemini-3-flash-reasoning%2Cgemma-4-31b%2Cgemini-3-1-pro-preview%2Cgemini-3-1-flash-lite-preview%2Cclaude-4-5-haiku-reasoning%2Cclaude-opus-4-6-adaptive%2Cclaude-sonnet-4-6-adaptive%2Cdeepseek-v3-2-reasoning%2Cgrok-4-20%2Cminimax-m2-7%2Cglm-5%2Cqwen3-5-397b-a17b%2Cqwen3-5-122b-a10b%2Cqwen3-5-35b-a3b%2Cqwen3-5-27b%2Cclaude-4-opus-thinking%2Cclaude-4-1-opus-thinking
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