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RTX A6000 48GB LLM Calculator — What Can It Run?

Check which models fit on a RTX A6000 48GB: max parameters per precision, cache headroom and estimated tokens/sec.

Memory needed (GB)
Headroom on this card (GB)
Max params at this quant (B)
Est. decode speed (tok/s)

RTX A6000 48GB: 48 GB, 768 GB/s, ~38.7 TFLOPS FP16. The A6000 (and its Ada successor) is the workstation bridge between consumer 24 GB and datacenter 80 GB: 48 GB runs 70B at 4-bit on ONE card — no parallelism, no second PSU, standard drivers.

Formula

needed = params × bpw ÷ 8 + reserve · fits if ≤ 48 GB · decode tok/s ≈ 0.6 × 768 GB/s ÷ weight-GB (bandwidth-bound)
References: NVIDIA/Apple official RTX A6000 48GB specifications; llama.cpp quantization size tables

About RTX A6000 48GB LLM Calculator — What Can It Run?

"Will it run?" is the first question of local AI, and for the RTX A6000 48GB this calculator answers it precisely: enter any model's parameter count and quantization and get the memory bill against this card's 48 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 768 GB/s is the speed limit that matters). The A6000 (and its Ada successor) is the workstation bridge between consumer 24 GB and datacenter 80 GB: 48 GB runs 70B at 4-bit on ONE card — no parallelism, no second PSU, standard drivers.

How to use RTX A6000 48GB LLM Calculator — What Can It Run?

  1. 1Enter your values into RTX A6000 48GB LLM Calculator — What Can It Run? — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
  2. 2The result recomputes live using the formula shown on the page; there is no button to press.
  3. 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.

Why use RTX A6000 48GB LLM Calculator — What Can It Run??

  • Computes RTX A6000 48GB 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

Why pick an A6000 over two 4090s with the same total VRAM?+

One memory pool beats two split ones: no tensor-parallel setup, no inter-GPU traffic, full 48 GB usable by a single 38 GB model. Two 4090s are faster when the split works cleanly, but the A6000 is the simpler, denser, quieter production choice.

What fits in 48 GB that doesn't in 24?+

Llama-3-70B/Qwen2.5-72B at Q4 (~38–40 GB) with real cache headroom, FP16 27–34B models, Mixtral 8x7B at Q6, and full-precision SDXL pipelines with refiners. Also 13B FULL fine-tuning (not just LoRA) squeezes in with ZeRO-2 offload.

How is the tokens/sec estimate for the RTX A6000 48GB derived?+

Decode is memory-bound: each token reads every weight once, so speed ≈ effective bandwidth ÷ model size. We assume ~60% of the 768 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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