Best local LLM for coding with 64GB VRAM
The models people report running for coding on setups with more than 48 GB and up to 64 GB of memory, such as NVIDIA CMP 170HX 64GB (unlocked) or AMD Instinct MI210. 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 Q4_0 Gen 53 t/s (1 run) · PP 762 t/s (1 run) · with speculation 44 t/s (1) | 8 | 53 1 run | 762 1 run | 44 1 run |
| 2 | Qwen3.827B Alibaba · mostly 4-bit Gen 50 t/s (1 run) · with speculation 33 t/s (3) | 8 | 50 1 run | no PP data | 33 3 runs |
| 3 | Qwen3.635B · 3B active Alibaba · mostly 4-bit Gen 71 t/s (1 run) · with speculation 103 t/s (2) | 3 | 71 1 run | no PP data | 103 2 runs |
| 4 | Qwen3.627B Alibaba · mostly 4-bit Gen 32 t/s (1 run) · with speculation 63 t/s (1) | 3 | 32 1 run | no PP data | 63 1 run |
| 5 | DeepSeek V4 Flash284B · 13B active DeepSeek · mostly Q2-Q4 mixed imatrix No generation data | 3 | no generation data | no PP data | none |
| 6 | Qwen3.8 Swift-1.527B Alibaba · mostly FP8 No generation data · with speculation 35 t/s (1) | 2 | no generation data | no PP data | 35 1 run |
| 7 | Gemma 426B · 4B active Google DeepMind Gen 97 t/s (1 run) | 1 | 97 1 run | no PP data | none |
| 8 | GLM-5.3 Flash320B · 18B active Zhipu AI · mostly IQ4_XS No generation data | 1 | no generation data | no PP data | none |
| 9 | Qwen3.8 Splash-HQ27B Alibaba · mostly Q8 No generation data · with speculation 37 t/s (1) | 1 | no generation data | no PP data | 37 1 run |
Frequently asked
What is the best local LLM for coding with 64GB of VRAM?
On setups with more than 48 GB and up to 64 GB of memory, the model people report using most for coding is Qwen3.8 125B · 6B active, followed by Qwen3.8 27B 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 64GB?
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.