RTX 5090 32GB LLM Calculator — What Can It Run?
Check which models fit on a RTX 5090 32GB: max parameters per precision, cache headroom and estimated tokens/sec.
RTX 5090 32GB: 32 GB, 1792 GB/s, ~105 TFLOPS FP16. The RTX 5090's GDDR7 delivers 1.79 TB/s — nearly A100-80GB territory — and 32 GB finally lets consumers run 32B-class models at high quant quality or 70B at 3-bit on one card.
Formula
About RTX 5090 32GB LLM Calculator — What Can It Run?
"Will it run?" is the first question of local AI, and for the RTX 5090 32GB this calculator answers it precisely: enter any model's parameter count and quantization and get the memory bill against this card's 32 GB, the largest model it can hold at that quant, and a bandwidth-derived decode-speed estimate (token generation streams the whole model per token, so 1792 GB/s is the speed limit that matters). The RTX 5090's GDDR7 delivers 1.79 TB/s — nearly A100-80GB territory — and 32 GB finally lets consumers run 32B-class models at high quant quality or 70B at 3-bit on one card.
How to use RTX 5090 32GB LLM Calculator — What Can It Run?
- 1Enter your values into RTX 5090 32GB LLM Calculator — What Can It Run? — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use RTX 5090 32GB LLM Calculator — What Can It Run??
- ✓Computes RTX 5090 32GB LLM instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: needed = params × bpw ÷ 8 + reserve.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
What does the 5090's 32 GB unlock over 24 GB cards?+
The 30B class at quality: Qwen2.5-32B/QwQ at Q5_K_M (~23 GB) with real context, FP16 14B models, and 70B at IQ3 quants (~28 GB) on a single card. It also fits QLoRA fine-tunes of 14B models that previously OOM'd at 24 GB.
How fast is 5090 decode compared to a 4090?+
Decode scales with bandwidth: 1792 vs 1008 GB/s suggests ~1.7× more tokens/s on the same quantized model, and benchmarks bear that out (e.g. ~60→100+ tok/s on 8B Q4). Prefill gains track the compute increase instead.
How is the tokens/sec estimate for the RTX 5090 32GB derived?+
Decode is memory-bound: each token reads every weight once, so speed ≈ effective bandwidth ÷ model size. We assume ~60% of the 1792 GB/s peak is achievable, matching llama.cpp benchmarks within ~20%. Prompt prefill is compute-bound and much faster per token.
Why reserve memory beyond the weights?+
The KV cache grows with context (use our per-model KV-cache calculators), CUDA/Metal runtimes take hundreds of MB, and allocator fragmentation wastes more. The default reserve suits 2–8K contexts; long-context work needs significantly more.
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