Best local LLM for coding with 16GB VRAM
The models people report running for coding on setups with more than 12 GB and up to 16 GB of memory, such as NVIDIA RTX 5060 Ti 16GB, NVIDIA RTX 5070 Ti, NVIDIA RTX 5080 or AMD RX 9070 XT 16GB. Each row is one model size, with the quant most people used and its typical speed.
Ranked from reports whose posters used the model for coding.
Ranked by community reports
| # | Model | Reports | Gen t/s | PP t/s | With speculation |
|---|---|---|---|---|---|
| 1 | Qwen3.827B Alibaba · mostly IQ4_XS Gen 29 t/s (5 runs) · PP 600 t/s (3 runs) · with speculation 64 t/s (7) | 29 | 29 5 runs, 5 devices | 600 3 runs | 64 7 runs |
| 2 | Qwen3.8 Flash-Next125B · 6B active Alibaba · mostly IQ3_S No generation data | 16 | no generation data | no PP data | none |
| 3 | Qwen3.635B · 3B active Alibaba · mostly IQ4_XS Gen 38 t/s (1 run) · PP 274 t/s (1 run) | 9 | 38 1 run | 274 1 run | none |
| 4 | Qwen3.8 Swift-1.527B Alibaba · mostly GSQ-RCO 3 xxs Gen 53 t/s (1 run) | 3 | 53 1 run | no PP data | none |
| 5 | Qwen38B Alibaba · mostly Q4_K_M Gen 131 t/s (1 run) | 1 | 131 1 run | no PP data | none |
| 6 | MiniCPM-V 4.735B · 3B active mostly mix_q3gud_im Gen 93 t/s (1 run) | 1 | 93 1 run | no PP data | none |
| 7 | Qwen3.8 Escha-W227B Alibaba · mostly 2.469 bpw Gen 59 t/s (1 run) | 1 | 59 1 run | no PP data | none |
| 8 | Gemma 412B Google DeepMind · mostly Q5_K_XL Gen 50 t/s (1 run) | 1 | 50 1 run | no PP data | none |
| 9 | Mimo 2.6 Distill-Qwen9B Xiaomi · mostly Q8_0 Gen 42 t/s (1 run) · PP 1718 t/s (1 run) | 1 | 42 1 run | 1718 1 run | none |
| 10 | Qwen3.6 APEX-I-Quality35B · 3B active Alibaba Gen 37 t/s (1 run) | 1 | 37 1 run | no PP data | none |
| 11 | Qwen3.6 Coder27B · 3B active Alibaba · mostly Q4_K_M Gen 14 t/s (1 run) | 1 | 14 1 run | no PP data | none |
| 12 | Gemma 4 E4B8B Google DeepMind · mostly 4bit Gen 8.2 t/s (1 run) | 1 | 8.2 1 run | no PP data | none |
| 13 | Gemma 431B Google DeepMind · mostly NVFP4 No generation data · with speculation 67 t/s (1) | 1 | no generation data | no PP data | 67 1 run |
| 14 | Mellum2.112B · 2.5B active mostly Q8 No generation data | 1 | no generation data | no PP data | none |
| 15 | Muse Glimmer30B Meta · mostly UD-Q2-K-XL No generation data · with speculation 20 t/s (1) | 1 | no generation data | no PP data | 20 1 run |
| 16 | Ornith1.59B mostly Q6_K No generation data | 1 | no generation data | no PP data | none |
| 17 | Ornith1.535B · 3B active mostly Q4_K_M No generation data | 1 | no generation data | no PP data | none |
| 18 | Qwen330B · 3B active Alibaba · mostly Q4_K_M No generation data | 1 | no generation data | no PP data | none |
| 19 | Qwen3.5 DeepSeek-V4-Flash9B Alibaba · mostly Q6_K No generation data · with speculation 49 t/s (1) | 1 | no generation data | no PP data | 49 1 run |
| 20 | Qwen3.627B Alibaba · mostly Q8 No generation data | 1 | no generation data | no PP data | none |
| 21 | Qwen3.8 abliterated27B Alibaba · mostly IQ3_S No generation data · with speculation 105 t/s (1) | 1 | no generation data | no PP data | 105 1 run |
| 22 | Qwen3.8 ASCII-Condensed27B Alibaba · mostly UD-IQ4_XS No generation data | 1 | no generation data | no PP data | none |
| 23 | Qwen3.8 Swift-1.5 Flash-Next125B · 6B active Alibaba · mostly IQ2_XS No generation data | 1 | no generation data | no PP data | none |
Frequently asked
What is the best local LLM for coding with 16GB of VRAM?
On setups with more than 12 GB and up to 16 GB of memory, the model people report using most for coding is Qwen3.8 27B, followed by Qwen3.8 125B · 6B active and Qwen3.6 35B · 3B active. The table shows the quant most of them used and their typical speed, so you can start from a setup that is known to work.
What counts as a coding report?
A community performance report whose poster described using the model for coding: an editor assistant, an agent, or code generation. The memory band is the poster's reported VRAM, or the card's VRAM times the number of cards.
Which quant should I use for coding on 16GB?
Start from the quant most people used for that model, shown under its name. It is one known to fit with room for a coding context. If you want a bigger model in the same memory, step down one quant rung, and the VRAM calculator shows the exact memory at each rung for your card.
How we rank
Model sizes are ordered by how many coding reports use them in this memory band, which measures what people run, not answer quality. Typical speed is the median of runs on one device serving one request without speculative decoding. Runs with MTP or a draft model are counted separately, because they can be several times faster. Reports come from r/LocalLLaMA, the web and direct submissions. Nothing is sponsored.