RTX 4090 24GB LLM Calculator — What Can It Run?
Check which models fit on a RTX 4090 24GB: max parameters per precision, cache headroom and estimated tokens/sec.
RTX 4090 24GB: 24 GB, 1008 GB/s, ~82.6 TFLOPS FP16. The RTX 4090 is the consumer flagship of the local-AI era: 24 GB at 1 TB/s with 82 TFLOPS — prompt prefill and fine-tuning fly, and anything up to ~30B at 4-bit runs with room for real context.
Formula
About RTX 4090 24GB LLM Calculator — What Can It Run?
"Will it run?" is the first question of local AI, and for the RTX 4090 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 1008 GB/s is the speed limit that matters). The RTX 4090 is the consumer flagship of the local-AI era: 24 GB at 1 TB/s with 82 TFLOPS — prompt prefill and fine-tuning fly, and anything up to ~30B at 4-bit runs with room for real context.
How to use RTX 4090 24GB LLM Calculator — What Can It Run?
- 1Enter your values into RTX 4090 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 4090 24GB LLM Calculator — What Can It Run??
- ✓Computes RTX 4090 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
What is the biggest model an RTX 4090 runs well?+
Sweet spots: 8B at FP16 with long context, 14B at Q6, 27–32B at Q4 (Gemma-2-27B, Qwen2.5-32B ~16–19 GB) with moderate context. 70B at extreme 2-bit quants technically loads but quality and cache room suffer — better on two cards.
4090 vs used dual 3090s at the same budget?+
Dual 3090s win on capacity (48 GB → 70B Q4 territory); a single 4090 wins on simplicity, power, resale and prefill speed. If your goal is the largest model possible, take the pair; for fastest 7–32B experience, the 4090.
How is the tokens/sec estimate for the RTX 4090 24GB derived?+
Decode is memory-bound: each token reads every weight once, so speed ≈ effective bandwidth ÷ model size. We assume ~60% of the 1008 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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