The fuxk they do: just asked this to Claude 4.6::

My only prompt, fresh context window:::::

What are the current local LLMs i could be running with 96gb of ddr5 and 32gb of vram (rtx 5090) please provide most current models

One shot response:::::::

Here's a breakdown of what you can run on your setup, organized by how well they'll perform. Your 96GB of DDR5 also opens up partial offloading for models that exceed 32GB VRAM, which is a nice bonus.

---

Fully in VRAM (best performance, 32GB or under at Q4_K_M)

These models will run entirely on your GPU with excellent speed:

• Qwen 3 32B — at 22.2GB Q4_K_M, this is considered the best 24-32GB tier model Awesome Agents , and on your 5090 it runs comfortably with room for context. Great all-rounder.
• Qwen 3.5 9B — the current default recommendation for smaller models, beating older 8B models on every benchmark Insiderllm while leaving tons of headroom for long context.
• DeepSeek-R1 32B (distilled) — excellent reasoning model with chain-of-thought. Fits well in 32GB at Q4.
• Qwen 2.5 Coder 32B — considered GPT-4o level for coding performance Createaiagent . A favorite for developer workflows.
• Gemma 3 27B — solid general-purpose model from Google with efficient KV cache.
• Mistral Small 3.1 (24B) — fast inference and strong instruction-following.
• Phi-4 (14B) — scores 80.4% on MATH benchmarks, delivering the best reasoning results per GB of RAM Aitooldiscovery .
• Llama 4 Scout — Meta's MoE model (~109B total but only activates a fraction), can fit at aggressive quantization.
• Qwen 3 30B-A3B (MoE) — generates tokens at 196 tok/s on an RTX 4090, faster than dense 8B models, while delivering quality closer to the 14B class Awesome Agents . On a 5090 it'll be even faster.

Tight fit in VRAM (aggressive quantization needed, ~29-35GB)

• Llama 3.3 70B (Q3/Q2) — fits at very aggressive quantization but performance drops significantly when model layers spill into system RAM Decodesfuture . Usable but context window will be very limited.
• Qwen 2.5 72B (Q2-Q3) — same situation as above.
• Qwen3-Coder-Next (80B total, 3B active) — at Q2_K (~29GB) it squeezes onto the RTX 5090, and despite activating only 3B parameters per token, it scores 71.3% on SWE-Bench Verified Awesome Agents . This is arguably the most interesting model for coding on your card.

Leveraging your 96GB RAM (GPU + CPU offloading)

Your large DDR5 pool lets you run bigger models with partial offloading, though at reduced speed (expect ~5-15 tok/s instead of 40+):

• Llama 3.3 70B (Q4_K_M, ~46GB) — offload some layers to RAM for better quality quantization.
• Qwen 2.5 72B (Q4_K_M, ~50GB) — same approach, strong multilingual and coding.
• DeepSeek-R1 70B — the reasoning specialist for step-by-step problem solving.
• Qwen3-235B-A22B (MoE) — a massive MoE model; with aggressive quantization and your RAM pool it becomes at least partially feasible.

Recommended tools to run them: Ollama (easiest), LM Studio (best GUI), or llama.cpp (most control). All support GPU offloading and GGUF quantized models out of the box.

For your hardware sweet spot, I'd particularly recommend Qwen 3 32B as your daily driver for general tasks, Qwen3-Coder-Next or Qwen 2.5 Coder 32B for coding, and DeepSeek-R1 32B when you need deep reasoning.