Flagship AI Workstation (RTX 5090 32GB)

Scored against every model in our directory at Q4_K_M, using the ~32GB of VRAM on this machine and its 1792 GB/s of memory bandwidth. Of 30 models, 23 run comfortably, 0 are a tight fit, 5 need CPU offload, and 2 will not load. The largest model that fits well here is Qwen3.6 35B-A3B (MoE).

How these are estimated: tokens/sec ≈ memory bandwidth × 0.65 ÷ active weights in GB. These are arithmetic estimates from published specifications, not lab measurements — BFWR does not run a hardware test lab. Real throughput varies with runner, quantization, context length and thermals.

Diffusion models size up differently from language models: the text encoder is often as large as the image model itself, and memory scales with resolution. Measured at 1024×1024, one image at a time, against the ~32GB of VRAM here, 11 of 11 open image models will run — the best being FLUX.2 [klein] 4B at BF16 / FP16, peaking near 18.8GB.

Weight sizes are read from the real .safetensors files in each model’s Hugging Face repository. Activation memory is an approximation that varies with the attention implementation and sampler, so leave headroom. These are planning figures, not benchmarks.

What is the largest model the Flagship AI Workstation (RTX 5090 32GB) can run?

Qwen3.6 35B-A3B (MoE) (36B (3B active)) is the largest model that fits, at roughly 21.6GB in Q4_K_M against 32GB of VRAM. It is rated "Comfortable" here, with an estimated 647.1 tokens/sec.

How many tokens per second should I expect?

Speed depends on the model, not just the machine. On this hardware the estimates range from about 647.1 tokens/sec for the smallest models down to single digits for the largest that still fit. The estimate uses memory bandwidth (1792 GB/s here) × 0.65 ÷ active weights in GB, and is not a measured benchmark.

Can the Flagship AI Workstation (RTX 5090 32GB) run a 70B model?

Not in a usable way. A 70B model needs roughly 40GB+ at Q4_K_M before context, which exceeds the 32GB of VRAM available here. A mixture-of-experts model is usually the better route on this machine, since only a fraction of its weights are active per token.

How much context can I use?

64k tokens is a sensible ceiling on this machine at FP16 / Q8_0 / Q4_K_M (70B). Context is stored in the KV cache, which grows with every token and competes with the model weights for the same memory, so a longer context means a smaller model.

Can the Flagship AI Workstation (RTX 5090 32GB) generate images with Flux or Stable Diffusion?

Yes. 11 of 11 open image models fit at 1024x1024, the best being FLUX.2 [klein] 4B at BF16 / FP16, peaking near 18.8GB. Higher resolutions and larger batches cost proportionally more.

Which runner works best on the Flagship AI Workstation (RTX 5090 32GB)?

Ollama, LM Studio, vLLM, llama.cpp, TensorRT-LLM are all expected to run on this hardware. Ollama is the simplest starting point, and `ollama run qwen3.6:35b` is a good first model for this machine.

In-depth reads that pair with this machine — sizing memory, picking a runner, and choosing quantization for ~32GB of usable memory.

• Best Budget GPUs for Local LLMs16GB cards, used RTX 3090s and current street prices.Read the guide
• RTX 5080 vs RTX 4090: Why 16GB VRAM Is the LimitWhy VRAM capacity beats raw speed for local inference.Read the guide
• How Much RAM and VRAM Do You Need to Run Local LLMs?Model weights, KV cache and OS overhead, tier by tier.Read the guide