Best local LLM for coding with 48GB VRAM
The models people report running for coding on setups with more than 32 GB and up to 48 GB of memory, such as NVIDIA A40 48GB, AMD Radeon Pro W7900, AMD Radeon Pro W7800 48GB or NVIDIA L40S. 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 4-bit No generation data · with speculation 63 t/s (6) | 19 | no generation data | no PP data | 63 6 runs |
| 2 | Qwen3.8 Flash-Next125B · 6B active Alibaba · mostly IQ3_S Gen 90 t/s (1 run) | 7 | 90 1 run | no PP data | none |
| 3 | Qwen3.627B Alibaba · mostly FP8 No generation data · with speculation 80 t/s (1) | 3 | no generation data | no PP data | 80 1 run |
| 4 | Qwen3.635B · 3B active Alibaba · mostly 4bit No generation data · with speculation 138 t/s (2) | 3 | no generation data | no PP data | 138 2 runs |
| 5 | Qwen3.8 Uncensored27B Alibaba · mostly 4-bit No generation data · with speculation 59 t/s (2) | 2 | no generation data | no PP data | 59 2 runs |
| 6 | DeepSeek V4 Flash 9B distill9B DeepSeek · mostly Q4_K_M Gen 44 t/s (1 run) | 1 | 44 1 run | no PP data | none |
| 7 | DeepSeek V4.1 Flash552B · 16B active mostly 2.6bit No generation data | 1 | no generation data | no PP data | none |
| 8 | Qwen3-Coder-Next80B · 3B active mostly 6-bit No generation data | 1 | no generation data | no PP data | none |
| 9 | Qwen3.54B Alibaba · mostly Q4_K_M No generation data | 1 | no generation data | no PP data | none |
| 10 | Qwen3.59B Alibaba · mostly 4-bit No generation data · with speculation 198 t/s (1) | 1 | no generation data | no PP data | 198 1 run |
| 11 | Qwen3.5122B · 10B active Alibaba · mostly UD-IQ4_XS No generation data | 1 | no generation data | no PP data | none |
| 12 | Qwen3.8 Swift-1.527B Alibaba · mostly Q6_K No generation data | 1 | no generation data | no PP data | none |
Frequently asked
What is the best local LLM for coding with 48GB of VRAM?
On setups with more than 32 GB and up to 48 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 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 48GB?
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.