Deepseek V4.1-1017 runs on RDNA GPUs with variable local throughput
A Reddit user tested Deepseek V4.1-1017 on an AMD Ryzen 3950X system with 96GB of DDR4 memory, a Radeon RX 7900 XTX, a Radeon Pro W6800, and a PCIe Gen 3 NVMe drive. Using mostly IQ2 weights with Q6 n-grams, the roughly 350GB model reached peaks of 19 tokens per second, usually produced about 13 tokens per second, and sometimes fell to 3; prefill was approximately 150 tokens per second. The tester attributed the large variation primarily to I/O needs rather than dspark hits and reported average decode speed of 9 tokens per second at the end of a long prompt.
The report provides a concrete, system-specific test of running a large Deepseek model across combined Radeon GPU memory and DDR4, offering useful reference data for local deployment planning.
Just a spec to performance report. Tried Maya today on the following:
- 3950x
- 96GB dual channel DDR4 at 3000mhz
- Rx 7900xtx 24GB + w6800 32GB (56GB together)
- pcie gen3 nvme drive
The Deepseek weights used were the mostly iq2 weights with Q6 ngrams. ~350GB on disk. Dspark left on.
I ended up getting wildly varying performance - less due to dspark hits and more due to when I/O was needed.
I got peaks as high as 19t/s, usually hovering around 13t/s, then dropping down to 3t/s for a few seconds, then right back up. Average at the end of a long prompt was 9t/s decode. Prefill is ~150 t/s. . Some prompts it rips through at 15 tokens a second, others it will crawl through at 3-5 tokens per second even if it's the same topic. Its very odd and I've been trying quite a few settings to figure it out.
The model itself is crazy smart - better than the iq4-xs quant of Qwen3.8-Flash-Next I've been running for certain - but it likes to think A LOT and with the already slow average of 9t/s decode I won't be using it for much outside of chatting even if it is the new best model on my machine.
submitted by /u/EmPips [link] [comments]
#Deepseek V4.1-1017#AMD Ryzen 9 3950X#Radeon RX 7900 XTX#Radeon Pro W6800#Qwen3.8-Flash-Next