Best local LLM for coding with 8GB VRAM
The models people report running for coding on setups with up to 8 GB of memory, such as NVIDIA RTX 3070, NVIDIA RTX 4060, NVIDIA RTX 5060 8GB or NVIDIA RTX 5060 Laptop 8GB. 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 | Qwen3.635B · 3B active Alibaba · mostly Q5_K_M No generation data | 5 | no generation data | no PP data | none |
| 2 | Ornith1.535B · 3B active mostly Q4_K_M No generation data | 4 | no generation data | no PP data | none |
| 3 | Qwen3.827B Alibaba · mostly IQ3_S No generation data | 3 | no generation data | no PP data | none |
| 4 | Qwen3.8 Flash-Next125B · 6B active Alibaba · mostly GSQ-RCO-IQ1_M No generation data | 3 | no generation data | no PP data | none |
| 5 | Qwen2.5 Coder-1.5B1.5B Alibaba Gen 99 t/s (1 run) | 1 | 99 1 run | no PP data | none |
| 6 | Qwen3.5 reasoning9B Alibaba Gen 50 t/s (1 run) | 1 | 50 1 run | no PP data | none |
| 7 | Qwen2.57B Alibaba Gen 34 t/s (1 run) | 1 | 34 1 run | no PP data | none |
| 8 | Bonsai 227B PrismML · mostly ternary Gen 7.6 t/s (1 run) · PP 42 t/s (1 run) | 1 | 7.6 1 run | 42 1 run | none |
| 9 | Mellum2.112B · 2.5B active mostly Q8 No generation data | 1 | no generation data | no PP data | none |
| 10 | Qwen3.627B Alibaba · mostly Q4_K_M No generation data | 1 | no generation data | no PP data | none |
Frequently asked
What is the best local LLM for coding with 8GB of VRAM?
On setups with up to 8 GB of memory, the model people report using most for coding is Qwen3.6 35B · 3B active, followed by Ornith1.5 35B · 3B active and Qwen3.8 27B. 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 8GB?
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.