16GB Is Enough For Local AI Until You Hit These Limits
Sources for every figure on screen, checked on 2026-10-09.
Prices
- RTX 5060 Ti 16GB, $799.99. MSI RTX 5060 Ti 16G Ventus 2X OC Plus, sold by Newegg, checked 2026-10-09.
https://www.newegg.com/p/N82E16814137958
- Used RTX 3090 24GB, going rate. RigPrice lists the used going rate at $1,623 on 2026-10-09 (84 active
listings, asking prices) and $1,600 on its 2026-10-07 snapshot. The video says “about $1,619”, within $4 of
today’s figure. https://rigprice.com/gpu/rtx-3090/
- $20 a month cloud plan. Claude Pro is $20 a month billed monthly. https://claude.com/pricing
| figure |
working |
| $819 gap |
1,619 − 800 |
| about 2x |
1,619 / 800 = 2.02 |
| $50 per GB |
800 / 16 |
| $67 per GB |
1,619 / 24 = 67.5 |
| about a third more per GB |
67.5 / 50 = 1.35 |
| $102 per added GB |
819 / 8 = 102.4 |
| $240 a year |
20 × 12 |
| 41 months |
819 / 20 = 40.95 |
| 81 months |
1,619 / 20 = 80.95, which is 6 years 9 months |
| 2.1x bandwidth |
936 / 448 = 2.09 |
| 13.27 GiB |
14.25 × 10⁹ bytes / 2³⁰ |
Memory bandwidth
- RTX 3090: 936 GB/s (RigPrice’s spec line for the card; NVIDIA’s published GeForce RTX 3090 spec).
https://rigprice.com/gpu/rtx-3090/
- RTX 5060 Ti: 448 GB/s (128 bit GDDR7 at 28 Gbps; NVIDIA’s published spec).
12B to 14B on 16 GB
- FitMyLLM: 14 models measured on one RTX 5060 Ti 16GB with llama.cpp; Llama 3.1 8B Instruct at Q4_K_M
decodes at 84.5 tok/s (single stream). https://www.fitmyllm.com/reports/rtx-5060-ti-16gb
- Craftrigs: Qwen3 14B at Q4_K, 32.9 tok/s on the RTX 5060 Ti 16GB (16K context; about 26 tok/s at 32K).
https://www.craftrigs.com/reviews/rtx-5060-ti-16gb-local-llm-review/
- InventiveHQ: Qwen2.5 Coder 14B, about 42 tok/s on the RTX 5060 Ti with speculative decoding in llama.cpp.
https://www.inventivehq.com/blog/llama-cpp-speculative-decoding-consumer-gpu
Qwen 3.8 27B
- Weights and KV cache (ComputingForGeeks): Q4_K_M weights 16.46 GB (about 16.5); KV cache 64 KiB per token
plus a 0.16 GB recurrent state; 2.3 GB of cache at 32K and 8.7 GB at 128K; 19.56 GB total at 32K including
0.8 GB runtime overhead. https://computingforgeeks.com/how-much-vram-to-run-llm/
- Measured VRAM by context (HardwareCorner): Q4_K Small build, 16.68 GiB; measured usage 20 GB at 32K and
26 GB at 128K. https://www.hardware-corner.net/qwen3-8-27b-hardware-tests/
- RTX 3090 speed (chinkeong.github.io): UD-IQ4_XS at short context, 41.5 to 43 tok/s; Q4_K_M about 40 tok/s;
llama.cpp. https://chinkeong.github.io/qwen-27b/index.html
- RTX 5060 Ti runs (r/LocalLLM): IQ4_XS file of 14.25 billion bytes; about 25.7 tok/s without speculative
decoding and about 47 tok/s with multi token prediction.
https://www.reddit.com/r/LocalLLM/comments/1voygm9/running_qwen3827b_dense_fully_on_a_single_rtx/
Qwen 3.5 35B A3B on 16 GB
- aidec blog: on an RTX 5060 Ti 16GB, the 4 bit build reached about 14 tok/s; a Q2_K_XL build reached
53.65 to 75.98 tok/s. https://blog.aidec.tw/post/qwen35b-a3b-test
Intel Arc B580
- 12 GB of VRAM (Intel’s published spec).
- Qwen3 14B: about 38 tok/s at Q4_K_M with llama.cpp’s SYCL backend.
https://gist.github.com/juniormotapf/83f3a389e70bffb22649e6a25898efeb
Not checked
- “An independent five day benchmark”: the chinkeong.github.io runs span several windows (8 days in August,
then 3 and 2 days in October); “five day” could not be matched to the page.
- The second Arc B580 figure (about 25 tok/s at Q5 and an 8K context) is not in the cited gist and no primary
source was found; it is not shown as a number on screen.
- The r/LocalLLM thread and the InventiveHQ page could not be fetched on 2026-10-09 (blocked to automated
fetches); their figures are as the script’s writer reported them.
- “CPU expert offload” for the 14 tok/s run: the aidec post reports the 4 bit speed but does not name the
offload method.