Best local LLM for coding with 96GB VRAM
The models people report running for coding on setups with more than 64 GB and up to 96 GB of memory, such as NVIDIA RTX Pro 6000 Blackwell or NVIDIA RTX PRO 6000 Max-Q. 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 | Qwen3.8 Flash-Next125B · 6B active Alibaba · mostly NVFP4 Gen 167 t/s (2 runs) · PP 4315 t/s (1 run) · with speculation 52 t/s (2) | 18 | 167 2 runs | 4315 1 run | 52 2 runs |
| 2 | Qwen3.827B Alibaba · mostly FP8 No generation data · with speculation 107 t/s (4) | 11 | no generation data | no PP data | 107 4 runs |
| 3 | DeepSeek V4 Flash284B · 13B active DeepSeek · mostly Q4_K_XL No generation data | 5 | no generation data | no PP data | none |
| 4 | Qwen3.627B Alibaba · mostly F16 No generation data · with speculation 18 t/s (2) | 3 | no generation data | no PP data | 18 2 runs |
| 5 | Qwen38B Alibaba · mostly Q4 Gen 43 t/s (1 run) | 1 | 43 1 run | no PP data | none |
| 6 | DeepSeek V4.1 Flash552B · 16B active mostly UD-Q4_K_XL No generation data | 1 | no generation data | no PP data | none |
| 7 | GLM-5.3 Flash320B · 18B active Zhipu AI · mostly 3 No generation data | 1 | no generation data | no PP data | none |
| 8 | Gemma 431B Google DeepMind No generation data · with speculation 125 t/s (1) | 1 | no generation data | no PP data | 125 1 run |
| 9 | Mimo 2.5310B · 15B active Xiaomi No generation data | 1 | no generation data | no PP data | none |
| 10 | Qwen3.635B · 3B active Alibaba No generation data | 1 | no generation data | no PP data | none |
| 11 | Qwen3.8 Swift-1.5 Flash-Next125B · 6B active Alibaba · mostly IQ3_S No generation data · with speculation 145 t/s (1) | 1 | no generation data | no PP data | 145 1 run |
Frequently asked
What is the best local LLM for coding with 96GB of VRAM?
On setups with more than 64 GB and up to 96 GB of memory, the model people report using most for coding is Qwen3.8 125B · 6B active, followed by Qwen3.8 27B and DeepSeek V4 Flash 284B · 13B 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 96GB?
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.