Best local LLM for coding with 12GB VRAM
The models people report running for coding on setups with more than 8 GB and up to 12 GB of memory, such as NVIDIA RTX 3060 12GB, NVIDIA RTX 4070, Intel Arc B580 12GB or NVIDIA RTX 5070. 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 IQ3_XXS No generation data · with speculation 29 t/s (3) | 11 | no generation data | no PP data | 29 3 runs |
| 2 | Qwen3.635B · 3B active Alibaba · mostly IQ4_XS No generation data | 5 | no generation data | no PP data | none |
| 3 | Qwen3.8 Flash-Next125B · 6B active Alibaba · mostly 4.27 bpw No generation data | 5 | no generation data | no PP data | none |
| 4 | Bonsai 227B PrismML · mostly PTQ1_0 Gen 49 t/s (3 runs) · PP 1211 t/s (3 runs) | 3 | 49 3 runs, 2 devices | 1211 3 runs | none |
| 5 | DeepSeek V4.1 Flash552B · 16B active mostly 2-bit No generation data | 2 | no generation data | no PP data | none |
| 6 | GLM-4.7 Flash30B Zhipu AI Gen 43 t/s (1 run) | 1 | 43 1 run | no PP data | none |
| 7 | Gemma 412B Google DeepMind Gen 28 t/s (1 run) | 1 | 28 1 run | no PP data | none |
| 8 | Agents-A1 Millie35B · 3B active InternScience · mostly ternary Gen 22 t/s (1 run) · PP 261 t/s (1 run) | 1 | 22 1 run | 261 1 run | none |
| 9 | KAT-Coder V2.5-Dev35B · 3B active Kwaipilot Gen 14 t/s (1 run) | 1 | 14 1 run | no PP data | none |
| 10 | Bonsai 2 Swift Katana27B PrismML · mostly PTQ1_0 No generation data | 1 | no generation data | no PP data | none |
| 11 | Bonsai 2 Ternary27B PrismML · mostly 2-bit ternary No generation data | 1 | no generation data | no PP data | none |
| 12 | DeepSeek V4 Flash REAP-150B150B DeepSeek · mostly FP4 No generation data | 1 | no generation data | no PP data | none |
| 13 | DeepSeek V4 Flash284B · 13B active DeepSeek · mostly IQ2_M No generation data | 1 | no generation data | no PP data | none |
| 14 | GLM-5.3 Flash320B · 18B active Zhipu AI · mostly Q4 No generation data | 1 | no generation data | no PP data | none |
| 15 | Qwen3.59B Alibaba · mostly Q4 No generation data | 1 | no generation data | no PP data | none |
| 16 | Qwen3.627B Alibaba · mostly Q2-XS No generation data | 1 | no generation data | no PP data | none |
| 17 | Qwen3.6 Tiel-Coder35B · 3B active Alibaba · mostly Q4_K_XL No generation data | 1 | no generation data | no PP data | none |
| 18 | Qwen3.8 L0xRE27B Alibaba · mostly IQ3 No generation data · with speculation 40 t/s (1) | 1 | no generation data | no PP data | 40 1 run |
| 19 | Qwen3.8 Swift-1.5-Uncensored27B Alibaba · mostly 2.3bpw No generation data | 1 | no generation data | no PP data | none |
Frequently asked
What is the best local LLM for coding with 12GB of VRAM?
On setups with more than 8 GB and up to 12 GB of memory, the model people report using most for coding is Qwen3.8 27B, followed by Qwen3.6 35B · 3B active and Qwen3.8 125B · 6B 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 12GB?
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.