A 24GB GPU can still host local LLMs, but you’ll feel the tradeoffs in model choice and latency. In the wild, people are testing Gemma 27B, Magistral Small 24B, and Qwen3-Coder-Flash-30B-A3B-Instruct, weighing speed against accuracy [1].
What fits in 24GB today? - Gemma 27B — fits for translation and coding tasks [1] - Magistral Small 24B — solid everyday work without breaking the bank [1] - Qwen3-Coder-Flash-30B-A3B-Instruct — strong for code generation [1] - Qwen3-32B — heftier problems, 64k context gains shown [1] - Memory note: a 3090 example hits ~23.3GB in use, underscoring tight VRAM budgets [1]
Single vs. multi-GPU reality — On high-end cards, RTX PRO 6000 can beat 4× RTX 5090 for 30B models like Qwen3-Coder-Flash-30B-A3B-Instruct (without tricks) [2]. But multi-GPU setups aren’t magic; disaggregation and fast interconnects matter, and 2×5090 scales well in some tests [2]. The trend: a single big card often wins for dense workloads; multi-GPU shines with proper tooling like vLLM when you can feed it data efficiently [2].
Budget-path reality — A €5,000 AI server is a ceiling many teams chase. The practical move: lease a cloud GPU first to test workflows, models, and batching, then decide on on‑prem needs. If you go on‑prem, options like EPYC or Threadripper with 4×3090 can surface, but second‑hand gear invites tinkering and warranty headaches [3].
Quantization reality — Providers vary: many run FP8 or even FP4; some claim higher accuracy but margins differ. Openrouter shows a mix of FP4/FP8, with real-world results still depending on the stack and model [4].
Bottom line: test in the cloud first, then choose a 7B/14B or 24GB-plus-offload path that matches your latency needs.
References
What is the best options currently available for a local LLM using a 24GB GPU?
User weighs local LLM options for translation and coding on 24GB GPUs, comparing Qwen, Gemma, Magistral, and GPT-OSS ecosystems today.
View sourceBenchmarking LLM Inference on RTX 4090 / RTX 5090 / RTX PRO 6000
Benchmarking LLM inference across GPUs, comparing 4090/5090 with Pro 6000; discusses scaling, vLLM, and reliability.
View source€5,000 AI server for LLM
Debates on on-prem versus cloud LLMs, budgets, hardware tradeoffs, GPU choices, parallelism, and model options like Qwen, GPT-OSS, Claude, in-depth.
View sourceApparently all third party providers downgrade, none of them provide a max quality model
Debate about model quantization (FP4/FP8), OpenRouter vs providers, accuracy versus cost, transparency, and benchmarking validity in LLM ecosystem today online
View source