RTX 3090 24GB LLM Calculator — What Can It Run?
Check which models fit on a RTX 3090 24GB: max parameters per precision, cache headroom and estimated tokens/sec.
RTX 3090 24GB: 24 GB, 936 GB/s, ~35.6 TFLOPS FP16. Used RTX 3090s are the local-LLM community's favorite deal: 24 GB of 936 GB/s memory — the same capacity class as a 4090 at a fraction of the price, and NVLink pairs combine into an effective 48 GB.
Formula
About RTX 3090 24GB LLM Calculator — What Can It Run?
"Will it run?" is the first question of local AI, and for the RTX 3090 24GB this calculator answers it precisely: enter any model's parameter count and quantization and get the memory bill against this card's 24 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 936 GB/s is the speed limit that matters). Used RTX 3090s are the local-LLM community's favorite deal: 24 GB of 936 GB/s memory — the same capacity class as a 4090 at a fraction of the price, and NVLink pairs combine into an effective 48 GB.
How to use RTX 3090 24GB LLM Calculator — What Can It Run?
- 1Enter your values into RTX 3090 24GB 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 3090 24GB LLM Calculator — What Can It Run??
- ✓Computes RTX 3090 24GB 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
Is a used 3090 still the best value for local AI?+
For LLM inference, usually yes: capacity and bandwidth are what decode speed needs, and the 3090 matches the 4090's 24 GB while its 936 GB/s is within 10%. The 4090's doubled compute mostly shows in prompt processing and diffusion, not chat decode.
Can two 3090s run 70B models?+
Yes — 2× 24 GB runs Llama-3-70B at 4-bit (~38 GB plus cache) with layers split across cards (device_map or tensor parallel). NVLink helps but PCIe works; expect 10–15 tokens/s, genuinely usable for a local 70B.
How is the tokens/sec estimate for the RTX 3090 24GB derived?+
Decode is memory-bound: each token reads every weight once, so speed ≈ effective bandwidth ÷ model size. We assume ~60% of the 936 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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