Hardware FAQ
RTX A4000 (16GB) — frequently asked questions
Direct answers to the questions local-LLM enthusiasts ask about RTX A4000 (16GB), drawn from 18 signed runs on llm-speed. Every numerical claim links to a verifiable run permalink at /r/<id>.
What's the fastest LLM on RTX A4000 (16GB)?
The fastest measured LLM on RTX A4000 (16GB) on llm-speed is LFM2.5-24B-A2B at 142.7 decode tok/s on llama.cpp 0.7.0-dev (build 1, commit b76b133) (workload concurrent-decode, run r_j5tgvia-kjk). Cite as https://llm-speed.com/r/r_j5tgvia-kjk.
This is the headline decode tokens-per-second across every (model, backend) pairing submitted on RTX A4000 (16GB); faster results may exist on hardware not yet benchmarked, but among signed runs this is the published top.
Can I run a 7B-class model on RTX A4000 (16GB)?
No 7B-class model run on RTX A4000 (16GB) has been submitted to llm-speed yet, so the canonical "yes/no with a measured tok/s" answer is not currently published.
RTX A4000 (16GB) should fit a 7B model at 4-bit quantization; submit a run with "llm-speed bench --models <hf-id>" to populate this answer.
Can I run a 30B-class model on RTX A4000 (16GB)?
Yes — llm-speed has measured Muse-Glimmer-30B-ctx8k at 23.3 decode tok/s on llama.cpp 0.7.0-dev (build 11803, commit a37cc4610) (workload chat-short, run r_u_kr0v-wwde) on RTX A4000 (16GB), confirming the 27-33B parameter range, including MoE 30B-A3B variants fits and runs end-to-end on this hardware. Cite as https://llm-speed.com/r/r_u_kr0v-wwde.
30B-class models on RTX A4000 (16GB) typically use UD-IQ3_XXS; check the run for the exact backend version and model digest.
Can I run a 70B-class model on RTX A4000 (16GB)?
No 70B-class model run on RTX A4000 (16GB) has been submitted to llm-speed yet, so the canonical "yes/no with a measured tok/s" answer is not currently published.
RTX A4000 (16GB) should fit a 70B model at 4-bit quantization; submit a run with "llm-speed bench --models <hf-id>" to populate this answer.
How does RTX A4000 (16GB) compare to RTX 5090 for local LLM inference?
For a head-to-head between RTX A4000 (16GB) and RTX 5090, see the side-by-side comparison page at https://llm-speed.com/vs/rtx-a4000-vs-rtx-5090, which lays out every (model, backend) pair where both rigs have a signed run.
As a single-rig anchor, RTX A4000 (16GB) tops out at 142.7 decode tok/s on LFM2.5-24B-A2B via llama.cpp (run r_j5tgvia-kjk); the RTX 5090 top number is on its own /hw/<slug> page so the comparison stays grounded in measured numbers, not extrapolation.
RTX A4000 (16GB) vs RTX 5090 (side-by-side) · RTX 5090 leaderboard · RTX A4000 (16GB) leaderboard
Which backend is fastest on RTX A4000 (16GB)?
The only backend with a signed local run on RTX A4000 (16GB) so far is llama.cpp, with a top result of 142.7 decode tok/s on LFM2.5-24B-A2B (run r_j5tgvia-kjk); a multi-backend comparison on RTX A4000 (16GB) is not yet published.
Submit a competing run with "llm-speed bench --backends <other-backend>" on a RTX A4000 (16GB) machine to populate the comparison.
Is RTX A4000 (16GB) worth it for local LLM inference in 2026?
On signed data, RTX A4000 (16GB) delivers up to 142.7 decode tok/s on LFM2.5-24B-A2B via llama.cpp (run r_j5tgvia-kjk), which puts it comfortably above interactive-coding thresholds (~80 tok/s) for the published top configuration.
"Worth it" depends on your model class: RTX A4000 (16GB) is most useful for 7B-class models and smaller; larger models will need quantization or a bigger rig.
For "what fits and how fast", the per-model rows on /hw/rtx-a4000 are the honest answer; for cross-rig comparisons, see /vs/rtx-a4000-vs-rtx-5090.
What quantization should I use on RTX A4000 (16GB)?
On RTX A4000 (16GB), the top decode rates by quant are Q4_K_M at 142.7 tok/s (LFM2.5-24B-A2B, run r_j5tgvia-kjk) and IQ3_XXS at 92.9 tok/s (Flint-40B-A2.5B-UD-ctx8k, run r_e8r7fxw1ke9); pick the smallest quant whose quality your task tolerates, since on memory-bandwidth-bound hardware decode speed scales roughly inversely with bytes-per-weight.
Quant choice is a quality-vs-speed tradeoff that this hardware FAQ does not arbitrate; llm-speed publishes hardware-side speed, not output quality. For quality scores, see the model card on Hugging Face and the LMSYS Chatbot Arena.
Q4_K_M: 142.7 tok/s (run r_j5tgvia-kjk) · IQ3_XXS: 92.9 tok/s (run r_e8r7fxw1ke9) · UD-IQ3_XXS: 23.3 tok/s (run r_u_kr0v-wwde)